Description of the procedures and analysis present in Manuscript 1, Independent morphological correlates to aging, Mild Cognitive Impairment, and Alzheimer’s Disease, at the Doctorate Thesis presented to the Programa de Pós-Graduação em Ciências Médicas at the Instituto D’Or de Pesquisa e Ensino as a partial requirement to obtain the Doctorate Degree.
Part of the data used here cannot be shared due to restrictions of the Ethic Committee. Data can be shared upon reasonable request to the corresponding author. To fulfill these limitation, we will generate random data to simulate the results.
Get in touch with us (fernandahmoraes@gmail.com) in case any help is needed, our aim is to improve the code as needed!
| Diagnostic | N | age | age_range | ESC | T | AT | AE | k | K | S | I |
|---|---|---|---|---|---|---|---|---|---|---|---|
| AD | 13 | 77 ± 6.1 | 63 ; 86 | 13 ± 3 | 2.4 ± 0.079 | 95000 ± 9300 | 37000 ± 3000 | 0.28 ± 0.01 | -0.55 ± 0.015 | 9.2 ± 0.13 | 10 ± 0.069 |
| MCI | 33 | 72 ± 4.6 | 62 ; 82 | 13 ± 2.4 | 2.5 ± 0.085 | 97000 ± 8500 | 37000 ± 2800 | 0.29 ± 0.0096 | -0.53 ± 0.014 | 9.2 ± 0.12 | 10 ± 0.063 |
| CTL | 77 | 66 ± 8.4 | 43 ; 80 | 15 ± 2.2 | 2.5 ± 0.099 | 98000 ± 7800 | 37000 ± 2400 | 0.3 ± 0.0095 | -0.52 ± 0.014 | 9.1 ± 0.1 | 10 ± 0.072 |
##
## Call:
## lm(formula = 1/2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
## data = dados_hemi_v1, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044853 -0.009505 0.000683 0.010895 0.035787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01789 0.14975 0.119 0.905
## log10(ExposedArea) 1.13042 0.03275 34.511 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01519 on 244 degrees of freedom
## Multiple R-squared: 0.83, Adjusted R-squared: 0.8293
## F-statistic: 1191 on 1 and 244 DF, p-value: < 2.2e-16
## [1] "Student's t test comapring slope with theoretical value 1.25. t = 3.65"
## [1] "Student's t test comapring slope with theoretical value 1.25. p value = 0.00032"
| Diagnostic | r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AD | 0.87 | 0.86 | 0.01 | 158.83 | 0 | 1 | 73.85 | -141.71 | -137.93 | 0.01 | 24 | 26 |
| MCI | 0.88 | 0.88 | 0.01 | 487.51 | 0 | 1 | 199.00 | -391.99 | -385.42 | 0.01 | 64 | 66 |
| CTL | 0.85 | 0.85 | 0.01 | 871.32 | 0 | 1 | 442.46 | -878.92 | -869.81 | 0.03 | 152 | 154 |
| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | 0.20 | 0.39 | 0.50 | 0.62 | -0.62 | 1.01 |
| AD | log10(ExposedArea) | 1.09 | 0.09 | 12.60 | 0.00 | 0.91 | 1.27 |
| MCI | (Intercept) | 0.53 | 0.21 | 2.50 | 0.02 | 0.11 | 0.95 |
| MCI | log10(ExposedArea) | 1.02 | 0.05 | 22.08 | 0.00 | 0.93 | 1.11 |
| CTL | (Intercept) | -0.23 | 0.18 | -1.27 | 0.21 | -0.60 | 0.13 |
| CTL | log10(ExposedArea) | 1.19 | 0.04 | 29.52 | 0.00 | 1.11 | 1.27 |
Slopes for each diagnostic:
##
## Pearson's product-moment correlation
##
## data: filter(lm_Age, term == "log10(ExposedArea)")$estimate and filter(lm_Age, term == "log10(ExposedArea)")$Age_interval
## t = -2.8822, df = 5, p-value = 0.0345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.96750265 -0.09146064
## sample estimates:
## cor
## -0.7901004
##
## Pearson's product-moment correlation
##
## data: filter(dados_hemi_v1, Diagnostic == "CTL")$K and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = -4.176, df = 152, p-value = 4.981e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4558437 -0.1713459
## sample estimates:
## cor
## -0.3208125
Age and diagnostic effects in cortical gyrification. We included 77 CTL (blue), 33 MCI (green) and 13 AD (red) subjects (A) Linear fitting for the model variables in each Diagnostic group, CTL (adjusted R²=0.85, and CTL (adjusted R²=0.097, MCI (adjusted R²=0.044, p=0.0051), p<0.0001), and AD (adjusted R²=0.86, p<0.0001), MCI (adjusted R²=0.88, p<0.0001). As the severity of the disease increase, the linear tendency is downshifted, with smaller linear intercepts (K). (B) K linear tendency with age with its 95% CI for the three diagnostics groups: AD (adjusted R²=0.026
##
## Pearson's product-moment correlation
##
## data: filter(dados_hemi_v1, Diagnostic == "CTL")$S and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = 1.546, df = 152, p-value = 0.1242
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03441253 0.27713228
## sample estimates:
## cor
## 0.1244254
##
## Pearson's product-moment correlation
##
## data: filter(dados_hemi_v1, Diagnostic == "CTL")$I and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = -6.6178, df = 152, p-value = 5.879e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5871863 -0.3402379
## sample estimates:
## cor
## -0.4729482
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 28.27 9.13e-12 ***
## Residuals 243 0.04819 0.000198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.015650636 0.007961177 0.02334010 0.0000083
## CTL-AD 0.021969964 0.014928714 0.02901121 0.0000000
## CTL-MCI 0.006319328 0.001433485 0.01120517 0.0071486
K decrease with age is shown on Figure 1 B. Cortical Thickness, Total area and Exposed area:
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00904 0.004519 14.71 9.49e-07 ***
## Residuals 239 0.07345 0.000307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014609191 0.0050152217 0.02420316 0.0011588
## CTL-AD 0.019888810 0.0111100645 0.02866756 0.0000006
## CTL-MCI 0.005279619 -0.0008538009 0.01141304 0.1072424
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01088 0.005439 10.07 6.3e-05 ***
## Residuals 239 0.12902 0.000540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014822783 0.002107549 0.02753802 0.0176045
## CTL-AD 0.021529149 0.009894360 0.03316394 0.0000563
## CTL-MCI 0.006706366 -0.001422478 0.01483521 0.1282589
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01254 0.006269 16.65 1.7e-07 ***
## Residuals 239 0.08998 0.000376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.021557695 0.010938941 0.032176449 0.0000088
## CTL-AD 0.023693603 0.013977151 0.033410055 0.0000001
## CTL-MCI 0.002135908 -0.004652656 0.008924473 0.7387226
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01975 0.009876 28.49 7.97e-12 ***
## Residuals 239 0.08284 0.000347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.01547690 0.005288318 0.02566549 0.0011957
## CTL-AD 0.02747185 0.018149018 0.03679469 0.0000000
## CTL-MCI 0.01199495 0.005481393 0.01850851 0.0000615
## # A tibble: 3 × 2
## Diagnostic N_SUBJ
## <fct> <int>
## 1 AD 13
## 2 MCI 33
## 3 CTL 77
## # A tibble: 3 × 2
## Diagnostic N_SUBJ
## <fct> <int>
## 1 AD 13
## 2 MCI 33
## 3 CTL 77
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 28.27 9.13e-12 ***
## Residuals 243 0.04819 0.000198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.015650636 0.007961177 0.02334010 0.0000083
## CTL-AD 0.021969964 0.014928714 0.02901121 0.0000000
## CTL-MCI 0.006319328 0.001433485 0.01120517 0.0071486
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00904 0.004519 14.71 9.49e-07 ***
## Residuals 239 0.07345 0.000307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014609191 0.0050152217 0.02420316 0.0011588
## CTL-AD 0.019888810 0.0111100645 0.02866756 0.0000006
## CTL-MCI 0.005279619 -0.0008538009 0.01141304 0.1072424
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01088 0.005439 10.07 6.3e-05 ***
## Residuals 239 0.12902 0.000540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014822783 0.002107549 0.02753802 0.0176045
## CTL-AD 0.021529149 0.009894360 0.03316394 0.0000563
## CTL-MCI 0.006706366 -0.001422478 0.01483521 0.1282589
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01254 0.006269 16.65 1.7e-07 ***
## Residuals 239 0.08998 0.000376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.021557695 0.010938941 0.032176449 0.0000088
## CTL-AD 0.023693603 0.013977151 0.033410055 0.0000001
## CTL-MCI 0.002135908 -0.004652656 0.008924473 0.7387226
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01975 0.009876 28.49 7.97e-12 ***
## Residuals 239 0.08284 0.000347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.01547690 0.005288318 0.02566549 0.0011957
## CTL-AD 0.02747185 0.018149018 0.03679469 0.0000000
## CTL-MCI 0.01199495 0.005481393 0.01850851 0.0000615
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00478 0.0023923 15.69 3.91e-07 ***
## Residuals 243 0.03706 0.0001525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.011591895 0.004849210 0.018334579 0.0001997
## CTL-AD 0.014605421 0.008431134 0.020779708 0.0000002
## CTL-MCI 0.003013526 -0.001270742 0.007297794 0.2233236
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00361 0.0018049 7.345 0.000802 ***
## Residuals 239 0.05873 0.0002457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.010592555 0.002013654 0.019171456 0.0109327
## CTL-AD 0.012750436 0.004900506 0.020600366 0.0004781
## CTL-MCI 0.002157881 -0.003326606 0.007642368 0.6231806
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00575 0.0028763 6.521 0.00175 **
## Residuals 239 0.10542 0.0004411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.011558848 6.491843e-05 0.02305278 0.0483683
## CTL-AD 0.015846744 5.329482e-03 0.02636401 0.0013265
## CTL-MCI 0.004287895 -3.060169e-03 0.01163596 0.3549990
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00604 0.003020 10.23 5.43e-05 ***
## Residuals 239 0.07051 0.000295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.0167424548 0.007342507 0.026142402 0.0001111
## CTL-AD 0.0157902985 0.007189088 0.024391509 0.0000651
## CTL-MCI -0.0009521562 -0.006961538 0.005057226 0.9259479
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00681 0.003405 13.31 3.31e-06 ***
## Residuals 239 0.06114 0.000256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.009981324 0.0012282029 0.01873445 0.0208566
## CTL-AD 0.016475471 0.0084661238 0.02448482 0.0000066
## CTL-MCI 0.006494146 0.0008982799 0.01209001 0.0182332
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01343 0.006717 25.3 1.05e-10 ***
## Residuals 243 0.06452 0.000265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01143 0.005714 16.86 1.41e-07 ***
## Residuals 239 0.08098 0.000339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.0043 0.002149 5.142 0.00651 **
## Residuals 239 0.0999 0.000418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00769 0.003843 11.8 1.3e-05 ***
## Residuals 239 0.07786 0.000326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.03180 0.015898 41.54 3.28e-16 ***
## Residuals 239 0.09146 0.000383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00333 0.0016661 9.025 0.000166 ***
## Residuals 243 0.04486 0.0001846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00259 0.0012967 5.359 0.00529 **
## Residuals 239 0.05782 0.0002419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00095 0.0004756 1.316 0.27
## Residuals 239 0.08639 0.0003615
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00089 0.0004448 2.067 0.129
## Residuals 239 0.05144 0.0002152
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01274 0.006371 22.82 8.51e-10 ***
## Residuals 239 0.06672 0.000279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## # A tibble: 60 × 8
## Contrast diff lwr upr `p adj` ROI variable agecorrection
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 CTL-AD 0.0358 0.0260 0.0456 4.41e-14 Temporal Lobe log[10]T no
## 2 CTL-AD 0.0275 0.0181 0.0368 1.04e-10 Temporal Lobe K no
## 3 CTL-AD 0.0237 0.0140 0.0334 8.08e- 8 Parietal Lobe K no
## 4 CTL-AD 0.0233 0.0151 0.0314 3.45e-10 Hemisphere log[10]T no
## 5 CTL-AD 0.0232 0.0149 0.0316 1.06e- 9 Temporal Lobe log[10]T yes
## 6 MCI-AD 0.0221 0.0114 0.0328 6.19e- 6 Temporal Lobe log[10]T no
## 7 CTL-AD 0.0220 0.0149 0.0290 8.55e-12 Hemisphere K no
## 8 CTL-AD 0.0217 0.0125 0.0309 2.18e- 7 Frontal Lobe log[10]T no
## 9 MCI-AD 0.0216 0.0109 0.0322 8.81e- 6 Parietal Lobe K no
## 10 CTL-AD 0.0215 0.00989 0.0332 5.63e- 5 Occipital Lobe K no
## # … with 50 more rows
Statistically significant (p<0.05) differences in mean levels with the 95% Confidence Interval of Diagnostics for K and log(AvgThickness), with (After age correction) and without (Raw data) age correction for the hemisphere and the four lobes. Multiple corrections were applied within each morphological feature and ROI.
We compared age intervals of 10 years to increase N at each comparison.
Linear model for visual inspection:
##
## Call:
## lm(formula = K ~ Age * ROI * Diagnostic, data = dados)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.062579 -0.012117 0.000376 0.012462 0.061381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.175e-01 4.632e-02 -9.013 <2e-16 ***
## Age -9.139e-04 5.991e-04 -1.525 0.1274
## ROIhemisphere -7.977e-02 6.551e-02 -1.218 0.2236
## ROIO 7.811e-02 6.551e-02 1.192 0.2333
## ROIP 1.091e-01 6.551e-02 1.665 0.0962 .
## ROIT 7.845e-02 6.551e-02 1.198 0.2313
## DiagnosticMCI -2.951e-02 5.784e-02 -0.510 0.6100
## DiagnosticCTL -1.502e-02 4.784e-02 -0.314 0.7535
## Age:ROIhemisphere 2.715e-04 8.472e-04 0.320 0.7487
## Age:ROIO 4.846e-04 8.472e-04 0.572 0.5674
## Age:ROIP 5.873e-05 8.472e-04 0.069 0.9448
## Age:ROIT 2.245e-04 8.472e-04 0.265 0.7911
## Age:DiagnosticMCI 5.356e-04 7.671e-04 0.698 0.4852
## Age:DiagnosticCTL 3.757e-04 6.254e-04 0.601 0.5481
## ROIhemisphere:DiagnosticMCI 4.889e-02 8.225e-02 0.594 0.5524
## ROIO:DiagnosticMCI 3.163e-02 8.179e-02 0.387 0.6990
## ROIP:DiagnosticMCI 5.957e-02 8.179e-02 0.728 0.4666
## ROIT:DiagnosticMCI 1.041e-01 8.179e-02 1.273 0.2033
## ROIhemisphere:DiagnosticCTL 2.221e-02 6.761e-02 0.328 0.7426
## ROIO:DiagnosticCTL 3.178e-02 6.765e-02 0.470 0.6387
## ROIP:DiagnosticCTL 1.799e-02 6.765e-02 0.266 0.7904
## ROIT:DiagnosticCTL 4.475e-02 6.765e-02 0.662 0.5084
## Age:ROIhemisphere:DiagnosticMCI -6.302e-04 1.091e-03 -0.578 0.5637
## Age:ROIO:DiagnosticMCI -4.052e-04 1.085e-03 -0.374 0.7088
## Age:ROIP:DiagnosticMCI -7.229e-04 1.085e-03 -0.666 0.5053
## Age:ROIT:DiagnosticMCI -1.413e-03 1.085e-03 -1.302 0.1931
## Age:ROIhemisphere:DiagnosticCTL -2.606e-04 8.839e-04 -0.295 0.7681
## Age:ROIO:DiagnosticCTL -3.753e-04 8.845e-04 -0.424 0.6714
## Age:ROIP:DiagnosticCTL -2.050e-04 8.845e-04 -0.232 0.8168
## Age:ROIT:DiagnosticCTL -5.254e-04 8.845e-04 -0.594 0.5526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01838 on 1192 degrees of freedom
## Multiple R-squared: 0.94, Adjusted R-squared: 0.9386
## F-statistic: 644.1 on 29 and 1192 DF, p-value: < 2.2e-16
## Analysis of Variance Table
##
## Response: K
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 0.0591 0.05908 174.8143 < 2.2e-16 ***
## ROI 4 6.2246 1.55614 4604.2704 < 2.2e-16 ***
## Diagnostic 2 0.0231 0.01157 34.2401 3.477e-15 ***
## Age:ROI 4 0.0029 0.00073 2.1533 0.07222 .
## Age:Diagnostic 2 0.0003 0.00014 0.4199 0.65722
## ROI:Diagnostic 8 0.0019 0.00024 0.7152 0.67830
## Age:ROI:Diagnostic 8 0.0009 0.00011 0.3256 0.95649
## Residuals 1192 0.4029 0.00034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Call:
## aov(formula = K ~ Diagnostic * ROI * Age_interval10, data = dados)
##
## Terms:
## Diagnostic ROI Age_interval10 Diagnostic:ROI
## Sum of Squares 0.058398 6.222467 0.036026 0.002912
## Deg. of Freedom 2 4 4 8
## Diagnostic:Age_interval10 ROI:Age_interval10
## Sum of Squares 0.008345 0.004742
## Deg. of Freedom 4 16
## Diagnostic:ROI:Age_interval10 Residuals
## Sum of Squares 0.005051 0.377744
## Deg. of Freedom 16 1167
##
## Residual standard error: 0.01799133
## 20 out of 75 effects not estimable
## Estimated effects may be unbalanced
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.058 0.0292 90.207 < 2e-16 ***
## ROI 4 6.222 1.5556 4805.912 < 2e-16 ***
## Age_interval10 4 0.036 0.0090 27.824 < 2e-16 ***
## Diagnostic:ROI 8 0.003 0.0004 1.125 0.344
## Diagnostic:Age_interval10 4 0.008 0.0021 6.446 3.95e-05 ***
## ROI:Age_interval10 16 0.005 0.0003 0.916 0.551
## Diagnostic:ROI:Age_interval10 16 0.005 0.0003 0.975 0.482
## Residuals 1167 0.378 0.0003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Call:
## aov(formula = K_age_decay ~ Diagnostic * ROI * Age_interval10,
## data = dados)
##
## Terms:
## Diagnostic ROI Age_interval10 Diagnostic:ROI
## Sum of Squares 0.023918 6.364823 0.009576 0.001663
## Deg. of Freedom 2 4 4 8
## Diagnostic:Age_interval10 ROI:Age_interval10
## Sum of Squares 0.006937 0.003000
## Deg. of Freedom 4 16
## Diagnostic:ROI:Age_interval10 Residuals
## Sum of Squares 0.004339 0.315550
## Deg. of Freedom 16 1167
##
## Residual standard error: 0.01644367
## 20 out of 75 effects not estimable
## Estimated effects may be unbalanced
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.024 0.0120 44.228 < 2e-16 ***
## ROI 4 6.365 1.5912 5884.764 < 2e-16 ***
## Age_interval10 4 0.010 0.0024 8.854 4.83e-07 ***
## Diagnostic:ROI 8 0.002 0.0002 0.769 0.631
## Diagnostic:Age_interval10 4 0.007 0.0017 6.414 4.18e-05 ***
## ROI:Age_interval10 16 0.003 0.0002 0.693 0.803
## Diagnostic:ROI:Age_interval10 16 0.004 0.0003 1.003 0.451
## Residuals 1167 0.316 0.0003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Difference of means in pairwise comparison for AD-CTL, AD-MCI, and MCI-CTL in grouped by age in decades in each ROI for (A) K and (B) K after age correction. Bars represents 95% confidence interval. There is no statistical power, probably influenced by the small number of observations in each data point, to infer that the difference between diagnostics is more significant in younger adults.
Optimal cut-off (maximum sensitivity + specificity) for K and Average Cortical Thickness including results with removed age effect (age correction). AD in red (N = 13), MCI in green (N = 33), and Cognitive Unimpaired Controls (CTL) in blue (N = 77). The dashed line represents optimal cut-off to discriminate AD and CTL, and the dotted line represents optimal cut-off to discriminate MCI and CTL. ACC - accuracy, SPEC - specificity, and SENS - sensibility. (A) The optimal cut-off for the CTL-AD contrast is -0.54 and CTL-MCI, -0.53. (B) The optimal cut-off for CTL-AD = -0.52 and CTL-MCI = -0.51. (C) The optimal cut-off for CTL-AD = 0.39 mm and CTL-MCI = 0.40 mm. (D) The optimal cut-off for CTL-AD = 0.43 mm and CTL-MCI = 0.44 mm.
##
## Call:
## lm(formula = 1/2 * logAvgThickness_age_decay + logTotalArea_age_decay ~
## logExposedArea_age_decay, data = dados_hemi_v1, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.032355 -0.008226 0.000773 0.008828 0.028420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.41298 0.13258 3.115 0.00206 **
## logExposedArea_age_decay 1.05030 0.02873 36.554 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01196 on 244 degrees of freedom
## Multiple R-squared: 0.8456, Adjusted R-squared: 0.845
## F-statistic: 1336 on 1 and 244 DF, p-value: < 2.2e-16
| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | 0.42 | 0.37 | 1.14 | 0.26 | -0.34 | 1.18 |
| AD | logExposedArea_age_decay | 1.05 | 0.08 | 13.07 | 0.00 | 0.88 | 1.21 |
| MCI | (Intercept) | 0.61 | 0.20 | 3.05 | 0.00 | 0.21 | 1.01 |
| MCI | logExposedArea_age_decay | 1.01 | 0.04 | 23.28 | 0.00 | 0.92 | 1.09 |
| CTL | (Intercept) | 0.18 | 0.18 | 1.00 | 0.32 | -0.17 | 0.53 |
| CTL | logExposedArea_age_decay | 1.10 | 0.04 | 28.77 | 0.00 | 1.03 | 1.18 |
##
## Kruskal-Wallis rank sum test
##
## data: estimate by Diagnostic
## Kruskal-Wallis chi-squared = 2, df = 2, p-value = 0.3679
| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | 0.20 | 0.39 | 0.50 | 0.62 | -0.62 | 1.01 |
| AD | logExposedArea | 1.09 | 0.09 | 12.60 | 0.00 | 0.91 | 1.27 |
| MCI | (Intercept) | 0.53 | 0.21 | 2.50 | 0.02 | 0.11 | 0.95 |
| MCI | logExposedArea | 1.02 | 0.05 | 22.08 | 0.00 | 0.93 | 1.11 |
| CTL | (Intercept) | -0.23 | 0.18 | -1.27 | 0.21 | -0.60 | 0.13 |
| CTL | logExposedArea | 1.19 | 0.04 | 29.52 | 0.00 | 1.11 | 1.27 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.058 0.0292 81.596 <2e-16 ***
## ROI 4 6.222 1.5556 4347.166 <2e-16 ***
## Diagnostic:ROI 8 0.003 0.0004 1.013 0.424
## Residuals 1207 0.432 0.0004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.1539 0.07697 6.275 0.0022 **
## Residuals 243 2.9810 0.01227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = S ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD -0.04832085 -0.10879738 0.012155678 0.1454395
## CTL-AD -0.07844671 -0.13382516 -0.023068259 0.0027747
## CTL-MCI -0.03012586 -0.06855234 0.008300619 0.1561058
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.1083 0.05415 11.21 2.2e-05 ***
## Residuals 243 1.1734 0.00483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = I ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.04158669 0.003644613 0.07952877 0.0277853
## CTL-AD 0.06616956 0.031425940 0.10091318 0.0000325
## CTL-MCI 0.02458287 0.000474665 0.04869107 0.0445049
Is it easier to diff diag when younger?
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00951 0.004754 24.206 3.90e-10 ***
## Age_interval 5 0.01008 0.002016 10.266 9.08e-09 ***
## Diagnostic:Age_interval 7 0.00581 0.000830 4.224 0.000228 ***
## Residuals 199 0.03909 0.000196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## diff lwr upr p adj
## MCI:[60,65)-AD:[60,65) 0.0247295988 -0.024817696 0.0742768933 9.504734e-01
## CTL:[60,65)-AD:[60,65) 0.0440618281 0.007948342 0.0801753144 3.252613e-03
## AD:[65,70)-AD:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[60,65) 0.0392822521 0.002536995 0.0760275091 2.283589e-02
## CTL:[65,70)-AD:[60,65) 0.0430458757 0.007050601 0.0790411506 4.534547e-03
## AD:[70,75)-AD:[60,65) 0.0202524211 -0.020202775 0.0607076177 9.491546e-01
## MCI:[70,75)-AD:[60,65) 0.0308792707 -0.005478504 0.0672370450 2.092742e-01
## CTL:[70,75)-AD:[60,65) 0.0228814145 -0.013169538 0.0589323666 7.217380e-01
## AD:[75,80)-AD:[60,65) 0.0179841586 -0.020395011 0.0563633279 9.721875e-01
## MCI:[75,80)-AD:[60,65) 0.0226882735 -0.015154098 0.0605306448 8.015808e-01
## CTL:[75,80)-AD:[60,65) 0.0332570904 -0.003673282 0.0701874633 1.347703e-01
## AD:[80,85)-AD:[60,65) 0.0183866342 -0.022068562 0.0588418308 9.793185e-01
## MCI:[80,85)-AD:[60,65) 0.0117412129 -0.028713984 0.0521964094 9.999045e-01
## CTL:[80,85)-AD:[60,65) 0.0383324981 -0.011214796 0.0878797926 3.636968e-01
## AD:[85,90)-AD:[60,65) -0.0052373330 -0.054784627 0.0443099615 1.000000e+00
## MCI:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[60,65)-MCI:[60,65) 0.0193322293 -0.016781257 0.0554457156 9.113170e-01
## AD:[65,70)-MCI:[60,65) NA NA NA NA
## MCI:[65,70)-MCI:[60,65) 0.0145526533 -0.022192604 0.0512979103 9.952006e-01
## CTL:[65,70)-MCI:[60,65) 0.0183162769 -0.017678998 0.0543115518 9.414707e-01
## AD:[70,75)-MCI:[60,65) -0.0044771777 -0.044932374 0.0359780189 1.000000e+00
## MCI:[70,75)-MCI:[60,65) 0.0061496719 -0.030208102 0.0425074462 1.000000e+00
## CTL:[70,75)-MCI:[60,65) -0.0018481843 -0.037899136 0.0342027678 1.000000e+00
## AD:[75,80)-MCI:[60,65) -0.0067454402 -0.045124609 0.0316337291 9.999999e-01
## MCI:[75,80)-MCI:[60,65) -0.0020413253 -0.039883697 0.0358010460 1.000000e+00
## CTL:[75,80)-MCI:[60,65) 0.0085274916 -0.028402881 0.0454578645 9.999964e-01
## AD:[80,85)-MCI:[60,65) -0.0063429646 -0.046798161 0.0341122320 1.000000e+00
## MCI:[80,85)-MCI:[60,65) -0.0129883859 -0.053443582 0.0274668106 9.996311e-01
## CTL:[80,85)-MCI:[60,65) 0.0136028994 -0.035944395 0.0631501939 9.999558e-01
## AD:[85,90)-MCI:[60,65) -0.0299669318 -0.079514226 0.0195803627 7.903899e-01
## MCI:[85,90)-MCI:[60,65) NA NA NA NA
## CTL:[85,90)-MCI:[60,65) NA NA NA NA
## AD:[65,70)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-CTL:[60,65) -0.0047795760 -0.018902728 0.0093435759 9.992738e-01
## CTL:[65,70)-CTL:[60,65) -0.0010159524 -0.013053781 0.0110218764 1.000000e+00
## AD:[70,75)-CTL:[60,65) -0.0238094069 -0.045851921 -0.0017668927 1.999484e-02
## MCI:[70,75)-CTL:[60,65) -0.0131825573 -0.026264501 -0.0001006135 4.598849e-02
## CTL:[70,75)-CTL:[60,65) -0.0211804136 -0.033383719 -0.0089771086 6.768297e-07
## AD:[75,80)-CTL:[60,65) -0.0260776695 -0.044027882 -0.0081274567 9.597817e-05
## MCI:[75,80)-CTL:[60,65) -0.0213735546 -0.038145393 -0.0046017161 1.506187e-03
## CTL:[75,80)-CTL:[60,65) -0.0108047376 -0.025402749 0.0037932740 4.465391e-01
## AD:[80,85)-CTL:[60,65) -0.0256751938 -0.047717708 -0.0036326796 6.871740e-03
## MCI:[80,85)-CTL:[60,65) -0.0323206152 -0.054363129 -0.0102781010 7.693327e-05
## CTL:[80,85)-CTL:[60,65) -0.0057293299 -0.041842816 0.0303841564 1.000000e+00
## AD:[85,90)-CTL:[60,65) -0.0492991610 -0.085412647 -0.0131856747 3.865864e-04
## MCI:[85,90)-CTL:[60,65) NA NA NA NA
## CTL:[85,90)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-AD:[65,70) NA NA NA NA
## AD:[70,75)-AD:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[65,70) NA NA NA NA
## CTL:[70,75)-AD:[65,70) NA NA NA NA
## AD:[75,80)-AD:[65,70) NA NA NA NA
## MCI:[75,80)-AD:[65,70) NA NA NA NA
## CTL:[75,80)-AD:[65,70) NA NA NA NA
## AD:[80,85)-AD:[65,70) NA NA NA NA
## MCI:[80,85)-AD:[65,70) NA NA NA NA
## CTL:[80,85)-AD:[65,70) NA NA NA NA
## AD:[85,90)-AD:[65,70) NA NA NA NA
## MCI:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-MCI:[65,70) 0.0037636236 -0.010054457 0.0175817039 9.999605e-01
## AD:[70,75)-MCI:[65,70) -0.0190298309 -0.042092841 0.0040331795 2.530061e-01
## MCI:[70,75)-MCI:[65,70) -0.0084029813 -0.023139578 0.0063336151 8.580698e-01
## CTL:[70,75)-MCI:[65,70) -0.0164008376 -0.030363311 -0.0024383639 6.032595e-03
## AD:[75,80)-MCI:[65,70) -0.0212980935 -0.040487678 -0.0021085088 1.389610e-02
## MCI:[75,80)-MCI:[65,70) -0.0165939785 -0.034686092 0.0014981354 1.155479e-01
## CTL:[75,80)-MCI:[65,70) -0.0060251616 -0.022122738 0.0100724147 9.975039e-01
## AD:[80,85)-MCI:[65,70) -0.0208956178 -0.043958628 0.0021673926 1.281810e-01
## MCI:[80,85)-MCI:[65,70) -0.0275410392 -0.050604050 -0.0044780287 4.640981e-03
## CTL:[80,85)-MCI:[65,70) -0.0009497539 -0.037695011 0.0357955031 1.000000e+00
## AD:[85,90)-MCI:[65,70) -0.0445195850 -0.081264842 -0.0077743280 3.658894e-03
## MCI:[85,90)-MCI:[65,70) NA NA NA NA
## CTL:[85,90)-MCI:[65,70) NA NA NA NA
## AD:[70,75)-CTL:[65,70) -0.0227934545 -0.044641758 -0.0009451513 3.086157e-02
## MCI:[70,75)-CTL:[65,70) -0.0121666049 -0.024918592 0.0005853822 8.071838e-02
## CTL:[70,75)-CTL:[65,70) -0.0201644612 -0.032013367 -0.0083155552 1.265567e-06
## AD:[75,80)-CTL:[65,70) -0.0250617171 -0.042772902 -0.0073505324 1.764978e-04
## MCI:[75,80)-CTL:[65,70) -0.0203576022 -0.036873367 -0.0038418373 2.731578e-03
## CTL:[75,80)-CTL:[65,70) -0.0097887852 -0.024091857 0.0045142867 5.940247e-01
## AD:[80,85)-CTL:[65,70) -0.0246592414 -0.046507545 -0.0028109382 1.097228e-02
## MCI:[80,85)-CTL:[65,70) -0.0313046628 -0.053152966 -0.0094563596 1.326660e-04
## CTL:[80,85)-CTL:[65,70) -0.0047133775 -0.040708652 0.0312818974 1.000000e+00
## AD:[85,90)-CTL:[65,70) -0.0482832086 -0.084278484 -0.0122879337 5.564458e-04
## MCI:[85,90)-CTL:[65,70) NA NA NA NA
## CTL:[85,90)-CTL:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[70,75) 0.0106268496 -0.011813656 0.0330673551 9.692717e-01
## CTL:[70,75)-AD:[70,75) 0.0026289933 -0.019310917 0.0245689041 1.000000e+00
## AD:[75,80)-AD:[70,75) -0.0022682625 -0.027854375 0.0233178503 1.000000e+00
## MCI:[75,80)-AD:[70,75) 0.0024358524 -0.022337795 0.0272094996 1.000000e+00
## CTL:[75,80)-AD:[70,75) 0.0130046693 -0.010352149 0.0363614879 8.804043e-01
## AD:[80,85)-AD:[70,75) -0.0018657869 -0.030471931 0.0267403569 1.000000e+00
## MCI:[80,85)-AD:[70,75) -0.0085112083 -0.037117352 0.0200949355 9.998660e-01
## CTL:[80,85)-AD:[70,75) 0.0180800770 -0.022375120 0.0585352736 9.825094e-01
## AD:[85,90)-AD:[70,75) -0.0254897541 -0.065944951 0.0149654424 7.328777e-01
## MCI:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[70,75)-MCI:[70,75) -0.0079978563 -0.020906168 0.0049104552 7.574760e-01
## AD:[75,80)-MCI:[70,75) -0.0128951121 -0.031331869 0.0055416444 5.538704e-01
## MCI:[75,80)-MCI:[70,75) -0.0081909972 -0.025482568 0.0091005735 9.691848e-01
## CTL:[75,80)-MCI:[70,75) 0.0023778197 -0.012814474 0.0175701137 1.000000e+00
## AD:[80,85)-MCI:[70,75) -0.0124926365 -0.034933142 0.0099478690 8.805340e-01
## MCI:[80,85)-MCI:[70,75) -0.0191380579 -0.041578563 0.0033024476 2.033763e-01
## CTL:[80,85)-MCI:[70,75) 0.0074532274 -0.028904547 0.0438110017 9.999994e-01
## AD:[85,90)-MCI:[70,75) -0.0361166037 -0.072474378 0.0002411705 5.369859e-02
## MCI:[85,90)-MCI:[70,75) NA NA NA NA
## CTL:[85,90)-MCI:[70,75) NA NA NA NA
## AD:[75,80)-CTL:[70,75) -0.0048972559 -0.022721324 0.0129268119 9.999553e-01
## MCI:[75,80)-CTL:[70,75) -0.0001931409 -0.016829902 0.0164436201 1.000000e+00
## CTL:[75,80)-CTL:[70,75) 0.0103756760 -0.004066941 0.0248182933 5.033192e-01
## AD:[80,85)-CTL:[70,75) -0.0044947802 -0.026434691 0.0174451306 9.999994e-01
## MCI:[80,85)-CTL:[70,75) -0.0111402016 -0.033080112 0.0107997092 9.425345e-01
## CTL:[80,85)-CTL:[70,75) 0.0154510837 -0.020599868 0.0515020358 9.886314e-01
## AD:[85,90)-CTL:[70,75) -0.0281187474 -0.064169700 0.0079322047 3.489203e-01
## MCI:[85,90)-CTL:[70,75) NA NA NA NA
## CTL:[85,90)-CTL:[70,75) NA NA NA NA
## MCI:[75,80)-AD:[75,80) 0.0047041149 -0.016510769 0.0259189990 9.999980e-01
## CTL:[75,80)-AD:[75,80) 0.0152729318 -0.004268785 0.0348146483 3.452659e-01
## AD:[80,85)-AD:[75,80) 0.0004024756 -0.025183637 0.0259885885 1.000000e+00
## MCI:[80,85)-AD:[75,80) -0.0062429457 -0.031829059 0.0193431671 9.999918e-01
## CTL:[80,85)-AD:[75,80) 0.0203483395 -0.018030830 0.0587275088 9.178325e-01
## AD:[85,90)-AD:[75,80) -0.0232214916 -0.061600661 0.0151576777 7.898732e-01
## MCI:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[75,80)-MCI:[75,80) 0.0105688169 -0.007896370 0.0290340033 8.542780e-01
## AD:[80,85)-MCI:[75,80) -0.0043016393 -0.029075287 0.0204720080 1.000000e+00
## MCI:[80,85)-MCI:[75,80) -0.0109470607 -0.035720708 0.0138265866 9.844039e-01
## CTL:[80,85)-MCI:[75,80) 0.0156442246 -0.022198147 0.0534865959 9.922725e-01
## AD:[85,90)-MCI:[75,80) -0.0279256065 -0.065767978 0.0099167648 4.522147e-01
## MCI:[85,90)-MCI:[75,80) NA NA NA NA
## CTL:[85,90)-MCI:[75,80) NA NA NA NA
## AD:[80,85)-CTL:[75,80) -0.0148704562 -0.038227275 0.0084863624 7.169454e-01
## MCI:[80,85)-CTL:[75,80) -0.0215158776 -0.044872696 0.0018409411 1.112848e-01
## CTL:[80,85)-CTL:[75,80) 0.0050754077 -0.031854965 0.0420057806 1.000000e+00
## AD:[85,90)-CTL:[75,80) -0.0384944234 -0.075424796 -0.0015640505 3.118513e-02
## MCI:[85,90)-CTL:[75,80) NA NA NA NA
## CTL:[85,90)-CTL:[75,80) NA NA NA NA
## MCI:[80,85)-AD:[80,85) -0.0066454214 -0.035251565 0.0219607224 9.999960e-01
## CTL:[80,85)-AD:[80,85) 0.0199458639 -0.020509333 0.0604010605 9.555534e-01
## AD:[85,90)-AD:[80,85) -0.0236239672 -0.064079164 0.0168312293 8.328823e-01
## MCI:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[80,85)-MCI:[80,85) 0.0265912853 -0.013863911 0.0670464818 6.651102e-01
## AD:[85,90)-MCI:[80,85) -0.0169785458 -0.057433742 0.0234766507 9.909039e-01
## MCI:[85,90)-MCI:[80,85) NA NA NA NA
## CTL:[85,90)-MCI:[80,85) NA NA NA NA
## AD:[85,90)-CTL:[80,85) -0.0435698311 -0.093117126 0.0059774634 1.626629e-01
## MCI:[85,90)-CTL:[80,85) NA NA NA NA
## CTL:[85,90)-CTL:[80,85) NA NA NA NA
## MCI:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-MCI:[85,90) NA NA NA NA
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00951 0.004754 24.206 3.90e-10 ***
## Age_interval 5 0.01008 0.002016 10.266 9.08e-09 ***
## Diagnostic:Age_interval 7 0.00581 0.000830 4.224 0.000228 ***
## Residuals 199 0.03909 0.000196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## diff lwr upr p adj
## MCI:[60,65)-AD:[60,65) 0.0247295988 -0.024817696 0.0742768933 9.504734e-01
## CTL:[60,65)-AD:[60,65) 0.0440618281 0.007948342 0.0801753144 3.252613e-03
## AD:[65,70)-AD:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[60,65) 0.0392822521 0.002536995 0.0760275091 2.283589e-02
## CTL:[65,70)-AD:[60,65) 0.0430458757 0.007050601 0.0790411506 4.534547e-03
## AD:[70,75)-AD:[60,65) 0.0202524211 -0.020202775 0.0607076177 9.491546e-01
## MCI:[70,75)-AD:[60,65) 0.0308792707 -0.005478504 0.0672370450 2.092742e-01
## CTL:[70,75)-AD:[60,65) 0.0228814145 -0.013169538 0.0589323666 7.217380e-01
## AD:[75,80)-AD:[60,65) 0.0179841586 -0.020395011 0.0563633279 9.721875e-01
## MCI:[75,80)-AD:[60,65) 0.0226882735 -0.015154098 0.0605306448 8.015808e-01
## CTL:[75,80)-AD:[60,65) 0.0332570904 -0.003673282 0.0701874633 1.347703e-01
## AD:[80,85)-AD:[60,65) 0.0183866342 -0.022068562 0.0588418308 9.793185e-01
## MCI:[80,85)-AD:[60,65) 0.0117412129 -0.028713984 0.0521964094 9.999045e-01
## CTL:[80,85)-AD:[60,65) 0.0383324981 -0.011214796 0.0878797926 3.636968e-01
## AD:[85,90)-AD:[60,65) -0.0052373330 -0.054784627 0.0443099615 1.000000e+00
## MCI:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[60,65)-MCI:[60,65) 0.0193322293 -0.016781257 0.0554457156 9.113170e-01
## AD:[65,70)-MCI:[60,65) NA NA NA NA
## MCI:[65,70)-MCI:[60,65) 0.0145526533 -0.022192604 0.0512979103 9.952006e-01
## CTL:[65,70)-MCI:[60,65) 0.0183162769 -0.017678998 0.0543115518 9.414707e-01
## AD:[70,75)-MCI:[60,65) -0.0044771777 -0.044932374 0.0359780189 1.000000e+00
## MCI:[70,75)-MCI:[60,65) 0.0061496719 -0.030208102 0.0425074462 1.000000e+00
## CTL:[70,75)-MCI:[60,65) -0.0018481843 -0.037899136 0.0342027678 1.000000e+00
## AD:[75,80)-MCI:[60,65) -0.0067454402 -0.045124609 0.0316337291 9.999999e-01
## MCI:[75,80)-MCI:[60,65) -0.0020413253 -0.039883697 0.0358010460 1.000000e+00
## CTL:[75,80)-MCI:[60,65) 0.0085274916 -0.028402881 0.0454578645 9.999964e-01
## AD:[80,85)-MCI:[60,65) -0.0063429646 -0.046798161 0.0341122320 1.000000e+00
## MCI:[80,85)-MCI:[60,65) -0.0129883859 -0.053443582 0.0274668106 9.996311e-01
## CTL:[80,85)-MCI:[60,65) 0.0136028994 -0.035944395 0.0631501939 9.999558e-01
## AD:[85,90)-MCI:[60,65) -0.0299669318 -0.079514226 0.0195803627 7.903899e-01
## MCI:[85,90)-MCI:[60,65) NA NA NA NA
## CTL:[85,90)-MCI:[60,65) NA NA NA NA
## AD:[65,70)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-CTL:[60,65) -0.0047795760 -0.018902728 0.0093435759 9.992738e-01
## CTL:[65,70)-CTL:[60,65) -0.0010159524 -0.013053781 0.0110218764 1.000000e+00
## AD:[70,75)-CTL:[60,65) -0.0238094069 -0.045851921 -0.0017668927 1.999484e-02
## MCI:[70,75)-CTL:[60,65) -0.0131825573 -0.026264501 -0.0001006135 4.598849e-02
## CTL:[70,75)-CTL:[60,65) -0.0211804136 -0.033383719 -0.0089771086 6.768297e-07
## AD:[75,80)-CTL:[60,65) -0.0260776695 -0.044027882 -0.0081274567 9.597817e-05
## MCI:[75,80)-CTL:[60,65) -0.0213735546 -0.038145393 -0.0046017161 1.506187e-03
## CTL:[75,80)-CTL:[60,65) -0.0108047376 -0.025402749 0.0037932740 4.465391e-01
## AD:[80,85)-CTL:[60,65) -0.0256751938 -0.047717708 -0.0036326796 6.871740e-03
## MCI:[80,85)-CTL:[60,65) -0.0323206152 -0.054363129 -0.0102781010 7.693327e-05
## CTL:[80,85)-CTL:[60,65) -0.0057293299 -0.041842816 0.0303841564 1.000000e+00
## AD:[85,90)-CTL:[60,65) -0.0492991610 -0.085412647 -0.0131856747 3.865864e-04
## MCI:[85,90)-CTL:[60,65) NA NA NA NA
## CTL:[85,90)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-AD:[65,70) NA NA NA NA
## AD:[70,75)-AD:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[65,70) NA NA NA NA
## CTL:[70,75)-AD:[65,70) NA NA NA NA
## AD:[75,80)-AD:[65,70) NA NA NA NA
## MCI:[75,80)-AD:[65,70) NA NA NA NA
## CTL:[75,80)-AD:[65,70) NA NA NA NA
## AD:[80,85)-AD:[65,70) NA NA NA NA
## MCI:[80,85)-AD:[65,70) NA NA NA NA
## CTL:[80,85)-AD:[65,70) NA NA NA NA
## AD:[85,90)-AD:[65,70) NA NA NA NA
## MCI:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-MCI:[65,70) 0.0037636236 -0.010054457 0.0175817039 9.999605e-01
## AD:[70,75)-MCI:[65,70) -0.0190298309 -0.042092841 0.0040331795 2.530061e-01
## MCI:[70,75)-MCI:[65,70) -0.0084029813 -0.023139578 0.0063336151 8.580698e-01
## CTL:[70,75)-MCI:[65,70) -0.0164008376 -0.030363311 -0.0024383639 6.032595e-03
## AD:[75,80)-MCI:[65,70) -0.0212980935 -0.040487678 -0.0021085088 1.389610e-02
## MCI:[75,80)-MCI:[65,70) -0.0165939785 -0.034686092 0.0014981354 1.155479e-01
## CTL:[75,80)-MCI:[65,70) -0.0060251616 -0.022122738 0.0100724147 9.975039e-01
## AD:[80,85)-MCI:[65,70) -0.0208956178 -0.043958628 0.0021673926 1.281810e-01
## MCI:[80,85)-MCI:[65,70) -0.0275410392 -0.050604050 -0.0044780287 4.640981e-03
## CTL:[80,85)-MCI:[65,70) -0.0009497539 -0.037695011 0.0357955031 1.000000e+00
## AD:[85,90)-MCI:[65,70) -0.0445195850 -0.081264842 -0.0077743280 3.658894e-03
## MCI:[85,90)-MCI:[65,70) NA NA NA NA
## CTL:[85,90)-MCI:[65,70) NA NA NA NA
## AD:[70,75)-CTL:[65,70) -0.0227934545 -0.044641758 -0.0009451513 3.086157e-02
## MCI:[70,75)-CTL:[65,70) -0.0121666049 -0.024918592 0.0005853822 8.071838e-02
## CTL:[70,75)-CTL:[65,70) -0.0201644612 -0.032013367 -0.0083155552 1.265567e-06
## AD:[75,80)-CTL:[65,70) -0.0250617171 -0.042772902 -0.0073505324 1.764978e-04
## MCI:[75,80)-CTL:[65,70) -0.0203576022 -0.036873367 -0.0038418373 2.731578e-03
## CTL:[75,80)-CTL:[65,70) -0.0097887852 -0.024091857 0.0045142867 5.940247e-01
## AD:[80,85)-CTL:[65,70) -0.0246592414 -0.046507545 -0.0028109382 1.097228e-02
## MCI:[80,85)-CTL:[65,70) -0.0313046628 -0.053152966 -0.0094563596 1.326660e-04
## CTL:[80,85)-CTL:[65,70) -0.0047133775 -0.040708652 0.0312818974 1.000000e+00
## AD:[85,90)-CTL:[65,70) -0.0482832086 -0.084278484 -0.0122879337 5.564458e-04
## MCI:[85,90)-CTL:[65,70) NA NA NA NA
## CTL:[85,90)-CTL:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[70,75) 0.0106268496 -0.011813656 0.0330673551 9.692717e-01
## CTL:[70,75)-AD:[70,75) 0.0026289933 -0.019310917 0.0245689041 1.000000e+00
## AD:[75,80)-AD:[70,75) -0.0022682625 -0.027854375 0.0233178503 1.000000e+00
## MCI:[75,80)-AD:[70,75) 0.0024358524 -0.022337795 0.0272094996 1.000000e+00
## CTL:[75,80)-AD:[70,75) 0.0130046693 -0.010352149 0.0363614879 8.804043e-01
## AD:[80,85)-AD:[70,75) -0.0018657869 -0.030471931 0.0267403569 1.000000e+00
## MCI:[80,85)-AD:[70,75) -0.0085112083 -0.037117352 0.0200949355 9.998660e-01
## CTL:[80,85)-AD:[70,75) 0.0180800770 -0.022375120 0.0585352736 9.825094e-01
## AD:[85,90)-AD:[70,75) -0.0254897541 -0.065944951 0.0149654424 7.328777e-01
## MCI:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[70,75)-MCI:[70,75) -0.0079978563 -0.020906168 0.0049104552 7.574760e-01
## AD:[75,80)-MCI:[70,75) -0.0128951121 -0.031331869 0.0055416444 5.538704e-01
## MCI:[75,80)-MCI:[70,75) -0.0081909972 -0.025482568 0.0091005735 9.691848e-01
## CTL:[75,80)-MCI:[70,75) 0.0023778197 -0.012814474 0.0175701137 1.000000e+00
## AD:[80,85)-MCI:[70,75) -0.0124926365 -0.034933142 0.0099478690 8.805340e-01
## MCI:[80,85)-MCI:[70,75) -0.0191380579 -0.041578563 0.0033024476 2.033763e-01
## CTL:[80,85)-MCI:[70,75) 0.0074532274 -0.028904547 0.0438110017 9.999994e-01
## AD:[85,90)-MCI:[70,75) -0.0361166037 -0.072474378 0.0002411705 5.369859e-02
## MCI:[85,90)-MCI:[70,75) NA NA NA NA
## CTL:[85,90)-MCI:[70,75) NA NA NA NA
## AD:[75,80)-CTL:[70,75) -0.0048972559 -0.022721324 0.0129268119 9.999553e-01
## MCI:[75,80)-CTL:[70,75) -0.0001931409 -0.016829902 0.0164436201 1.000000e+00
## CTL:[75,80)-CTL:[70,75) 0.0103756760 -0.004066941 0.0248182933 5.033192e-01
## AD:[80,85)-CTL:[70,75) -0.0044947802 -0.026434691 0.0174451306 9.999994e-01
## MCI:[80,85)-CTL:[70,75) -0.0111402016 -0.033080112 0.0107997092 9.425345e-01
## CTL:[80,85)-CTL:[70,75) 0.0154510837 -0.020599868 0.0515020358 9.886314e-01
## AD:[85,90)-CTL:[70,75) -0.0281187474 -0.064169700 0.0079322047 3.489203e-01
## MCI:[85,90)-CTL:[70,75) NA NA NA NA
## CTL:[85,90)-CTL:[70,75) NA NA NA NA
## MCI:[75,80)-AD:[75,80) 0.0047041149 -0.016510769 0.0259189990 9.999980e-01
## CTL:[75,80)-AD:[75,80) 0.0152729318 -0.004268785 0.0348146483 3.452659e-01
## AD:[80,85)-AD:[75,80) 0.0004024756 -0.025183637 0.0259885885 1.000000e+00
## MCI:[80,85)-AD:[75,80) -0.0062429457 -0.031829059 0.0193431671 9.999918e-01
## CTL:[80,85)-AD:[75,80) 0.0203483395 -0.018030830 0.0587275088 9.178325e-01
## AD:[85,90)-AD:[75,80) -0.0232214916 -0.061600661 0.0151576777 7.898732e-01
## MCI:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[75,80)-MCI:[75,80) 0.0105688169 -0.007896370 0.0290340033 8.542780e-01
## AD:[80,85)-MCI:[75,80) -0.0043016393 -0.029075287 0.0204720080 1.000000e+00
## MCI:[80,85)-MCI:[75,80) -0.0109470607 -0.035720708 0.0138265866 9.844039e-01
## CTL:[80,85)-MCI:[75,80) 0.0156442246 -0.022198147 0.0534865959 9.922725e-01
## AD:[85,90)-MCI:[75,80) -0.0279256065 -0.065767978 0.0099167648 4.522147e-01
## MCI:[85,90)-MCI:[75,80) NA NA NA NA
## CTL:[85,90)-MCI:[75,80) NA NA NA NA
## AD:[80,85)-CTL:[75,80) -0.0148704562 -0.038227275 0.0084863624 7.169454e-01
## MCI:[80,85)-CTL:[75,80) -0.0215158776 -0.044872696 0.0018409411 1.112848e-01
## CTL:[80,85)-CTL:[75,80) 0.0050754077 -0.031854965 0.0420057806 1.000000e+00
## AD:[85,90)-CTL:[75,80) -0.0384944234 -0.075424796 -0.0015640505 3.118513e-02
## MCI:[85,90)-CTL:[75,80) NA NA NA NA
## CTL:[85,90)-CTL:[75,80) NA NA NA NA
## MCI:[80,85)-AD:[80,85) -0.0066454214 -0.035251565 0.0219607224 9.999960e-01
## CTL:[80,85)-AD:[80,85) 0.0199458639 -0.020509333 0.0604010605 9.555534e-01
## AD:[85,90)-AD:[80,85) -0.0236239672 -0.064079164 0.0168312293 8.328823e-01
## MCI:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[80,85)-MCI:[80,85) 0.0265912853 -0.013863911 0.0670464818 6.651102e-01
## AD:[85,90)-MCI:[80,85) -0.0169785458 -0.057433742 0.0234766507 9.909039e-01
## MCI:[85,90)-MCI:[80,85) NA NA NA NA
## CTL:[85,90)-MCI:[80,85) NA NA NA NA
## AD:[85,90)-CTL:[80,85) -0.0435698311 -0.093117126 0.0059774634 1.626629e-01
## MCI:[85,90)-CTL:[80,85) NA NA NA NA
## CTL:[85,90)-CTL:[80,85) NA NA NA NA
## MCI:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-MCI:[85,90) NA NA NA NA
Is it easier to diff diag when younger?
| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | -0.038 | 0.069 | -0.546 | 0.586 | -0.175 | 0.100 |
| AD | logExposedArea_corrected | 1.139 | 0.016 | 72.633 | 0.000 | 1.108 | 1.170 |
| MCI | (Intercept) | -0.092 | 0.043 | -2.157 | 0.032 | -0.176 | -0.008 |
| MCI | logExposedArea_corrected | 1.155 | 0.010 | 120.088 | 0.000 | 1.136 | 1.174 |
| CTL | (Intercept) | -0.108 | 0.030 | -3.658 | 0.000 | -0.166 | -0.050 |
| CTL | logExposedArea_corrected | 1.160 | 0.007 | 173.625 | 0.000 | 1.147 | 1.173 |
| ROI | Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|
| F | AD | (Intercept) | 0.09 | 0.51 | 0.17 | 0.87 | -0.96 | 1.13 |
| F | AD | logExposedArea_corrected | 1.11 | 0.11 | 9.74 | 0.00 | 0.87 | 1.34 |
| F | MCI | (Intercept) | 0.41 | 0.22 | 1.82 | 0.07 | -0.04 | 0.85 |
| F | MCI | logExposedArea_corrected | 1.04 | 0.05 | 20.81 | 0.00 | 0.94 | 1.14 |
| F | CTL | (Intercept) | -0.07 | 0.19 | -0.36 | 0.72 | -0.45 | 0.31 |
| F | CTL | logExposedArea_corrected | 1.14 | 0.04 | 26.40 | 0.00 | 1.06 | 1.23 |
| O | AD | (Intercept) | 0.39 | 0.41 | 0.95 | 0.35 | -0.46 | 1.23 |
| O | AD | logExposedArea_corrected | 1.04 | 0.10 | 10.60 | 0.00 | 0.84 | 1.24 |
| O | MCI | (Intercept) | 0.39 | 0.18 | 2.19 | 0.03 | 0.03 | 0.75 |
| O | MCI | logExposedArea_corrected | 1.04 | 0.04 | 24.18 | 0.00 | 0.95 | 1.13 |
| O | CTL | (Intercept) | -0.03 | 0.15 | -0.17 | 0.87 | -0.32 | 0.27 |
| O | CTL | logExposedArea_corrected | 1.14 | 0.04 | 31.72 | 0.00 | 1.07 | 1.21 |
| P | AD | (Intercept) | 0.51 | 0.28 | 1.85 | 0.08 | -0.06 | 1.08 |
| P | AD | logExposedArea_corrected | 1.02 | 0.06 | 17.12 | 0.00 | 0.90 | 1.15 |
| P | MCI | (Intercept) | 0.37 | 0.20 | 1.83 | 0.07 | -0.03 | 0.78 |
| P | MCI | logExposedArea_corrected | 1.06 | 0.04 | 23.83 | 0.00 | 0.97 | 1.15 |
| P | CTL | (Intercept) | 0.28 | 0.14 | 1.99 | 0.05 | 0.00 | 0.56 |
| P | CTL | logExposedArea_corrected | 1.08 | 0.03 | 35.14 | 0.00 | 1.02 | 1.14 |
| T | AD | (Intercept) | 0.63 | 0.39 | 1.65 | 0.11 | -0.16 | 1.43 |
| T | AD | logExposedArea_corrected | 0.99 | 0.09 | 11.41 | 0.00 | 0.81 | 1.17 |
| T | MCI | (Intercept) | 0.24 | 0.19 | 1.23 | 0.22 | -0.15 | 0.63 |
| T | MCI | logExposedArea_corrected | 1.08 | 0.04 | 24.85 | 0.00 | 0.99 | 1.17 |
| T | CTL | (Intercept) | 0.12 | 0.16 | 0.76 | 0.45 | -0.20 | 0.45 |
| T | CTL | logExposedArea_corrected | 1.11 | 0.04 | 30.31 | 0.00 | 1.04 | 1.18 |
## diag x
## 1 AD 1.042579
## 2 MCI 1.043525
## 3 CTL 1.038388
| ROI | Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|
| F | AD | (Intercept) | 0.52 | 0.40 | 1.31 | 0.20 | -0.30 | 1.35 |
| F | AD | logExposedArea_age_decay | 1.02 | 0.09 | 10.75 | 0.00 | 0.82 | 1.21 |
| F | MCI | (Intercept) | 0.70 | 0.22 | 3.25 | 0.00 | 0.27 | 1.14 |
| F | MCI | logExposedArea_age_decay | 0.98 | 0.05 | 19.06 | 0.00 | 0.87 | 1.08 |
| F | CTL | (Intercept) | 0.42 | 0.17 | 2.55 | 0.01 | 0.10 | 0.75 |
| F | CTL | logExposedArea_age_decay | 1.04 | 0.04 | 26.59 | 0.00 | 0.97 | 1.12 |
| O | AD | (Intercept) | 0.72 | 0.39 | 1.85 | 0.08 | -0.09 | 1.53 |
| O | AD | logExposedArea_age_decay | 0.96 | 0.10 | 9.16 | 0.00 | 0.74 | 1.18 |
| O | MCI | (Intercept) | 0.47 | 0.17 | 2.70 | 0.01 | 0.12 | 0.81 |
| O | MCI | logExposedArea_age_decay | 1.03 | 0.05 | 22.12 | 0.00 | 0.94 | 1.13 |
| O | CTL | (Intercept) | 0.81 | 0.13 | 6.17 | 0.00 | 0.55 | 1.07 |
| O | CTL | logExposedArea_age_decay | 0.94 | 0.04 | 26.69 | 0.00 | 0.87 | 1.01 |
| P | AD | (Intercept) | 0.20 | 0.32 | 0.62 | 0.54 | -0.47 | 0.87 |
| P | AD | logExposedArea_age_decay | 1.11 | 0.08 | 13.77 | 0.00 | 0.95 | 1.28 |
| P | MCI | (Intercept) | 0.87 | 0.23 | 3.73 | 0.00 | 0.40 | 1.33 |
| P | MCI | logExposedArea_age_decay | 0.95 | 0.06 | 16.41 | 0.00 | 0.84 | 1.07 |
| P | CTL | (Intercept) | 0.45 | 0.17 | 2.64 | 0.01 | 0.11 | 0.78 |
| P | CTL | logExposedArea_age_decay | 1.06 | 0.04 | 25.02 | 0.00 | 0.97 | 1.14 |
| T | AD | (Intercept) | 0.49 | 0.40 | 1.22 | 0.23 | -0.34 | 1.33 |
| T | AD | logExposedArea_age_decay | 1.04 | 0.10 | 10.14 | 0.00 | 0.83 | 1.25 |
| T | MCI | (Intercept) | 0.80 | 0.19 | 4.28 | 0.00 | 0.43 | 1.18 |
| T | MCI | logExposedArea_age_decay | 0.96 | 0.05 | 20.28 | 0.00 | 0.87 | 1.06 |
| T | CTL | (Intercept) | 0.29 | 0.18 | 1.61 | 0.11 | -0.07 | 0.64 |
| T | CTL | logExposedArea_age_decay | 1.09 | 0.05 | 23.96 | 0.00 | 1.00 | 1.18 |
## diag ROI x
## 1 AD F 1.0171086
## 2 MCI F 0.9764493
## 3 CTL F 1.0434639
## 4 AD O 0.9610541
## 5 MCI O 1.0319401
## 6 CTL O 0.9410686
## 7 AD P 1.1135527
## 8 MCI P 0.9516712
## 9 CTL P 1.0561775
## 10 AD T 1.0359612
## 11 MCI T 0.9606870
## 12 CTL T 1.0920563
| ROI | Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|
| F | AD | (Intercept) | 0.52 | 0.40 | 1.31 | 0.20 | -0.30 | 1.35 |
| F | AD | logExposedArea_age_decay | 1.02 | 0.09 | 10.75 | 0.00 | 0.82 | 1.21 |
| F | MCI | (Intercept) | 0.70 | 0.22 | 3.25 | 0.00 | 0.27 | 1.14 |
| F | MCI | logExposedArea_age_decay | 0.98 | 0.05 | 19.06 | 0.00 | 0.87 | 1.08 |
| F | CTL | (Intercept) | 0.42 | 0.17 | 2.55 | 0.01 | 0.10 | 0.75 |
| F | CTL | logExposedArea_age_decay | 1.04 | 0.04 | 26.59 | 0.00 | 0.97 | 1.12 |
| O | AD | (Intercept) | 0.72 | 0.39 | 1.85 | 0.08 | -0.09 | 1.53 |
| O | AD | logExposedArea_age_decay | 0.96 | 0.10 | 9.16 | 0.00 | 0.74 | 1.18 |
| O | MCI | (Intercept) | 0.47 | 0.17 | 2.70 | 0.01 | 0.12 | 0.81 |
| O | MCI | logExposedArea_age_decay | 1.03 | 0.05 | 22.12 | 0.00 | 0.94 | 1.13 |
| O | CTL | (Intercept) | 0.81 | 0.13 | 6.17 | 0.00 | 0.55 | 1.07 |
| O | CTL | logExposedArea_age_decay | 0.94 | 0.04 | 26.69 | 0.00 | 0.87 | 1.01 |
| P | AD | (Intercept) | 0.20 | 0.32 | 0.62 | 0.54 | -0.47 | 0.87 |
| P | AD | logExposedArea_age_decay | 1.11 | 0.08 | 13.77 | 0.00 | 0.95 | 1.28 |
| P | MCI | (Intercept) | 0.87 | 0.23 | 3.73 | 0.00 | 0.40 | 1.33 |
| P | MCI | logExposedArea_age_decay | 0.95 | 0.06 | 16.41 | 0.00 | 0.84 | 1.07 |
| P | CTL | (Intercept) | 0.45 | 0.17 | 2.64 | 0.01 | 0.11 | 0.78 |
| P | CTL | logExposedArea_age_decay | 1.06 | 0.04 | 25.02 | 0.00 | 0.97 | 1.14 |
| T | AD | (Intercept) | 0.49 | 0.40 | 1.22 | 0.23 | -0.34 | 1.33 |
| T | AD | logExposedArea_age_decay | 1.04 | 0.10 | 10.14 | 0.00 | 0.83 | 1.25 |
| T | MCI | (Intercept) | 0.80 | 0.19 | 4.28 | 0.00 | 0.43 | 1.18 |
| T | MCI | logExposedArea_age_decay | 0.96 | 0.05 | 20.28 | 0.00 | 0.87 | 1.06 |
| T | CTL | (Intercept) | 0.29 | 0.18 | 1.61 | 0.11 | -0.07 | 0.64 |
| T | CTL | logExposedArea_age_decay | 1.09 | 0.05 | 23.96 | 0.00 | 1.00 | 1.18 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 32.072 4.84e-13 ***
## Age_interval10 4 0.00595 0.001488 8.508 1.99e-06 ***
## Diagnostic:Age_interval10 4 0.00116 0.000289 1.652 0.162
## Residuals 235 0.04109 0.000175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## diff lwr upr p adj
## AD:[60,70)-CTL:[40,50) -0.04120537 -0.07703904 -0.0053717114 8.972574e-03
## AD:[70,80)-CTL:[40,50) -0.02683462 -0.04646153 -0.0072077197 4.533307e-04
## MCI:[70,80)-CTL:[40,50) -0.01807119 -0.03570285 -0.0004395211 3.841218e-02
## AD:[80,90)-CTL:[40,50) -0.04980700 -0.07247020 -0.0271438040 1.112003e-10
## MCI:[80,90)-CTL:[40,50) -0.03425295 -0.05873200 -0.0097738963 2.796721e-04
## AD:[70,80)-CTL:[50,60) -0.01813722 -0.03276625 -0.0035081902 2.784514e-03
## AD:[80,90)-CTL:[50,60) -0.04110960 -0.05961402 -0.0226051750 6.702661e-11
## MCI:[80,90)-CTL:[50,60) -0.02555554 -0.04624412 -0.0048669700 2.961629e-03
## AD:[80,90)-MCI:[60,70) -0.03228902 -0.05100254 -0.0135754971 1.205537e-06
## AD:[70,80)-CTL:[60,70) -0.01400774 -0.02660211 -0.0014133700 1.422265e-02
## AD:[80,90)-CTL:[60,70) -0.03698012 -0.05392188 -0.0200383548 1.523291e-10
## MCI:[80,90)-CTL:[60,70) -0.02142606 -0.04072960 -0.0021225208 1.461375e-02
## AD:[80,90)-AD:[70,80) -0.02297238 -0.04259928 -0.0033454720 6.926793e-03
## AD:[80,90)-MCI:[70,80) -0.03173582 -0.04936748 -0.0141041520 3.077961e-07
## AD:[80,90)-CTL:[70,80) -0.03269266 -0.04990660 -0.0154787280 4.689844e-08
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00478 0.0023923 16.351 2.25e-07 ***
## Age_interval10 4 0.00168 0.0004212 2.878 0.0235 *
## Diagnostic:Age_interval10 4 0.00099 0.0002468 1.687 0.1537
## Residuals 235 0.03438 0.0001463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## diff lwr upr p adj
## AD:[80,90)-CTL:[40,50) -0.02876988 -0.04950203 -0.008037726 3.334192e-04
## AD:[80,90)-CTL:[50,60) -0.02416190 -0.04108963 -0.007234172 1.816362e-04
## AD:[80,90)-MCI:[60,70) -0.02216979 -0.03928880 -0.005050780 1.295358e-03
## AD:[80,90)-CTL:[60,70) -0.02530581 -0.04080402 -0.009807590 6.202620e-06
## AD:[80,90)-MCI:[70,80) -0.02369527 -0.03982461 -0.007565935 9.433146e-05
## AD:[80,90)-CTL:[70,80) -0.02504436 -0.04079156 -0.009297159 1.283490e-05
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 28.933 5.53e-12 ***
## Age.group 1 0.00147 0.001472 7.595 0.0063 **
## Diagnostic:Age.group 2 0.00021 0.000103 0.532 0.5883
## Residuals 240 0.04651 0.000194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic * Age.group, data = dados_hemi_v1_DACTL)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.015650636 0.008048585 0.02325269 0.0000065
## CTL-AD 0.021969964 0.015008754 0.02893117 0.0000000
## CTL-MCI 0.006319328 0.001489023 0.01114963 0.0064052
##
## $Age.group
## diff lwr upr p adj
## 76-86-66-75 -0.00532469 -0.009494614 -0.001154767 0.0125435
##
## $`Diagnostic:Age.group`
## diff lwr upr p adj
## MCI:66-75-AD:66-75 0.0132027422 -0.002070637 0.028476122 0.1331506
## CTL:66-75-AD:66-75 0.0175295279 0.002973143 0.032085912 0.0083090
## AD:76-86-AD:66-75 -0.0071676449 -0.024162293 0.009827003 0.8308950
## MCI:76-86-AD:66-75 0.0039835621 -0.013011086 0.020978210 0.9847044
## CTL:76-86-AD:66-75 0.0135118232 -0.003219326 0.030242972 0.1900489
## CTL:66-75-MCI:66-75 0.0043267857 -0.002400958 0.011054529 0.4372864
## AD:76-86-MCI:66-75 -0.0203703871 -0.031424448 -0.009316327 0.0000040
## MCI:76-86-MCI:66-75 -0.0092191801 -0.020273241 0.001834880 0.1617523
## CTL:76-86-MCI:66-75 0.0003090811 -0.010335427 0.010953589 0.9999994
## AD:76-86-CTL:66-75 -0.0246971728 -0.034737315 -0.014657030 0.0000000
## MCI:76-86-CTL:66-75 -0.0135459658 -0.023586108 -0.003505823 0.0018865
## CTL:76-86-CTL:66-75 -0.0040177047 -0.013605079 0.005569670 0.8346759
## MCI:76-86-AD:76-86 0.0111512070 -0.002180492 0.024482906 0.1593089
## CTL:76-86-AD:76-86 0.0206794682 0.007685336 0.033673601 0.0001122
## CTL:76-86-MCI:76-86 0.0095282612 -0.003465871 0.022522394 0.2873585
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.1539 0.07697 6.470 0.00183 **
## Age.group 1 0.0226 0.02262 1.901 0.16925
## Diagnostic:Age.group 2 0.1029 0.05145 4.324 0.01429 *
## Residuals 240 2.8555 0.01190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = S ~ Diagnostic * Age.group, data = dados_hemi_v1_DACTL)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD -0.04832085 -0.1078839 0.011242168 0.1371341
## CTL-AD -0.07844671 -0.1329887 -0.023904762 0.0023323
## CTL-MCI -0.03012586 -0.0679719 0.007720179 0.1475039
##
## $Age.group
## diff lwr upr p adj
## 76-86-66-75 0.02087186 -0.01180001 0.05354373 0.2094573
##
## $`Diagnostic:Age.group`
## diff lwr upr p adj
## MCI:66-75-AD:66-75 -0.0953105293 -0.214979358 0.024358299 0.2029580
## CTL:66-75-AD:66-75 -0.0981648521 -0.212215936 0.015886232 0.1364256
## AD:76-86-AD:66-75 -0.0303712353 -0.163526415 0.102783945 0.9864784
## MCI:76-86-AD:66-75 -0.0001112472 -0.133266427 0.133043933 1.0000000
## CTL:76-86-AD:66-75 -0.1082371945 -0.239327828 0.022853439 0.1703321
## CTL:66-75-MCI:66-75 -0.0028543228 -0.055567029 0.049858384 0.9999872
## AD:76-86-MCI:66-75 0.0649392940 -0.021670645 0.151549233 0.2634887
## MCI:76-86-MCI:66-75 0.0951992821 0.008589343 0.181809221 0.0218904
## CTL:76-86-MCI:66-75 -0.0129266652 -0.096327707 0.070474377 0.9977733
## AD:76-86-CTL:66-75 0.0677936168 -0.010872150 0.146459384 0.1354651
## MCI:76-86-CTL:66-75 0.0980536048 0.019387838 0.176719372 0.0054879
## CTL:76-86-CTL:66-75 -0.0100723424 -0.085190617 0.065045932 0.9988905
## MCI:76-86-AD:76-86 0.0302599881 -0.074195529 0.134715505 0.9612657
## CTL:76-86-AD:76-86 -0.0778659592 -0.179676603 0.023944685 0.2428766
## CTL:76-86-MCI:76-86 -0.1081259473 -0.209936592 -0.006315303 0.0301371
| Diagnostic | N | Mean_COGNITIVE_INDEX | STD_COGNITIVE_INDEX | Mean_A7_A5 | STD_A7_A5 | Mean_TMT_B_A | STD_TMT_B_A | Mean_relogio | STD_relogio | Mean_DIGIT_SPAN_BACK | STD_DIGIT_SPAN_BACK | Mean_Lipoxina | STD_Lipoxina | Mean_AB1_40 | STD_AB1_40 | Mean_AB1_42 | STD_AB1_42 | Mean_TAU | STD_TAU |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AD | 13 | -3.35 | 1.48 | 0.24 | 0.31 | 226.69 | 131.29 | 8.92 | 1.64 | 3.77 | 1.39 | 79.10 | 73.64 | 5664.22 | 1665.88 | 279.71 | 60.00 | 632.00 | 278.83 |
| MCI | 33 | -1.48 | 1.28 | 0.54 | 0.30 | 129.73 | 105.03 | 8.61 | 1.84 | 4.70 | 1.60 | 120.24 | 49.46 | 4557.04 | 2559.94 | 413.35 | 306.30 | 444.21 | 196.85 |
| CTL | 77 | 0.21 | 0.65 | 0.82 | 0.18 | 58.53 | 48.00 | 9.29 | 1.21 | 5.84 | 1.74 | 127.15 | 61.52 | 4192.04 | 1915.04 | 533.92 | 242.82 | 354.87 | 194.95 |
| clinical_test | Diagnostic | N | Mean | STD |
|---|---|---|---|---|
| A7/A5 | AD | 13 | 0.24 | 0.31 |
| A7/A5 | MCI | 33 | 0.54 | 0.30 |
| A7/A5 | CTL | 77 | 0.82 | 0.18 |
| AB1_ratio | AD | 13 | 0.05 | 0.01 |
| AB1_ratio | MCI | 33 | 0.12 | 0.12 |
| AB1_ratio | CTL | 77 | 0.16 | 0.12 |
| AB1-40 | AD | 13 | 5664.22 | 1611.83 |
| AB1-40 | MCI | 33 | 4557.04 | 2522.38 |
| AB1-40 | CTL | 77 | 4192.04 | 1902.56 |
| AB1-42 | AD | 13 | 279.71 | 58.05 |
| AB1-42 | MCI | 33 | 413.35 | 301.81 |
| AB1-42 | CTL | 77 | 533.92 | 241.24 |
| COGNITIVE_INDEX | AD | 13 | -3.35 | 1.46 |
| COGNITIVE_INDEX | MCI | 33 | -1.48 | 1.27 |
| COGNITIVE_INDEX | CTL | 77 | 0.21 | 0.64 |
| DIGIT SPAN BACK | AD | 13 | 3.77 | 1.37 |
| DIGIT SPAN BACK | MCI | 33 | 4.70 | 1.59 |
| DIGIT SPAN BACK | CTL | 77 | 5.84 | 1.74 |
| Lipoxina | AD | 13 | 79.10 | 71.25 |
| Lipoxina | MCI | 33 | 120.24 | 48.73 |
| Lipoxina | CTL | 77 | 127.15 | 61.11 |
| TAU | AD | 13 | 632.00 | 269.78 |
| TAU | MCI | 33 | 444.21 | 193.97 |
| TAU | CTL | 77 | 354.87 | 193.68 |
| TAU_AB1_42_ratio | AD | 13 | 2.20 | 0.55 |
| TAU_AB1_42_ratio | MCI | 33 | 1.60 | 1.41 |
| TAU_AB1_42_ratio | CTL | 77 | 0.79 | 0.60 |
| TAU_AB1_ratio | AD | 13 | 13027.12 | 6874.25 |
| TAU_AB1_ratio | MCI | 33 | 7216.44 | 6823.18 |
| TAU_AB1_ratio | CTL | 77 | 3429.86 | 3575.63 |
| TMT B-A | AD | 13 | 226.69 | 129.37 |
| TMT B-A | MCI | 33 | 129.73 | 104.43 |
| TMT B-A | CTL | 77 | 58.53 | 47.88 |
| morphological_parameter | clinical_test | t | df | Correlation | pvalue | Diagnostic | ROI | Age_correction |
|---|---|---|---|---|---|---|---|---|
| K | A7/A5 | 5.80 | 240 | 0.35 | 0.00 | All | Hemisphere | no |
| K | COGNITIVE_INDEX | 6.70 | 240 | 0.40 | 0.00 | All | Hemisphere | no |
| K | DIGIT SPAN BACK | 4.10 | 240 | 0.25 | 0.00 | All | Hemisphere | no |
| K | TMT B-A | -4.80 | 240 | -0.29 | 0.00 | All | Hemisphere | no |
| K_age_decay | A7/A5 | 4.30 | 240 | 0.26 | 0.00 | All | Hemisphere | yes |
| K_age_decay | COGNITIVE_INDEX | 4.90 | 240 | 0.30 | 0.00 | All | Hemisphere | yes |
| K_age_decay | DIGIT SPAN BACK | 3.10 | 240 | 0.19 | 0.00 | All | Hemisphere | yes |
| K_age_decay | TMT B-A | -3.10 | 240 | -0.19 | 0.00 | All | Hemisphere | yes |
| logAvgThickness | A7/A5 | 6.70 | 240 | 0.39 | 0.00 | All | Hemisphere | no |
| logAvgThickness | COGNITIVE_INDEX | 6.80 | 240 | 0.40 | 0.00 | All | Hemisphere | no |
| logAvgThickness | DIGIT SPAN BACK | 3.20 | 240 | 0.20 | 0.00 | All | Hemisphere | no |
| logAvgThickness | TMT B-A | -3.50 | 240 | -0.22 | 0.00 | All | Hemisphere | no |
| logAvgThickness_age_decay | A7/A5 | 4.20 | 240 | 0.26 | 0.00 | All | Hemisphere | yes |
| logAvgThickness_age_decay | COGNITIVE_INDEX | 4.10 | 240 | 0.26 | 0.00 | All | Hemisphere | yes |
| logAvgThickness_age_decay | DIGIT SPAN BACK | 1.80 | 240 | 0.11 | 0.08 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TMT B-A | -1.10 | 240 | -0.07 | 0.28 | All | Hemisphere | yes |
| K | AB1_ratio | 1.70 | 94 | 0.18 | 0.08 | All | Hemisphere | no |
| K | AB1-40 | -0.76 | 94 | -0.08 | 0.45 | All | Hemisphere | no |
| K | AB1-42 | 2.50 | 94 | 0.25 | 0.02 | All | Hemisphere | no |
| K | Lipoxina | 0.85 | 92 | 0.09 | 0.40 | All | Hemisphere | no |
| K | TAU | -2.60 | 94 | -0.26 | 0.01 | All | Hemisphere | no |
| K | TAU_AB1_42_ratio | -3.20 | 94 | -0.31 | 0.00 | All | Hemisphere | no |
| K | TAU_AB1_ratio | -2.80 | 94 | -0.28 | 0.01 | All | Hemisphere | no |
| K_age_decay | AB1_ratio | 1.60 | 94 | 0.16 | 0.12 | All | Hemisphere | yes |
| K_age_decay | AB1-40 | -0.28 | 94 | -0.03 | 0.78 | All | Hemisphere | yes |
| K_age_decay | AB1-42 | 2.40 | 94 | 0.24 | 0.02 | All | Hemisphere | yes |
| K_age_decay | Lipoxina | 1.00 | 92 | 0.11 | 0.31 | All | Hemisphere | yes |
| K_age_decay | TAU | -1.70 | 94 | -0.17 | 0.09 | All | Hemisphere | yes |
| K_age_decay | TAU_AB1_42_ratio | -2.30 | 94 | -0.23 | 0.02 | All | Hemisphere | yes |
| K_age_decay | TAU_AB1_ratio | -2.00 | 94 | -0.20 | 0.05 | All | Hemisphere | yes |
| logAvgThickness | AB1_ratio | 2.00 | 94 | 0.20 | 0.05 | All | Hemisphere | no |
| logAvgThickness | AB1-40 | -2.10 | 94 | -0.21 | 0.04 | All | Hemisphere | no |
| logAvgThickness | AB1-42 | 0.84 | 94 | 0.09 | 0.40 | All | Hemisphere | no |
| logAvgThickness | Lipoxina | -0.51 | 92 | -0.05 | 0.61 | All | Hemisphere | no |
| logAvgThickness | TAU | -4.30 | 94 | -0.41 | 0.00 | All | Hemisphere | no |
| logAvgThickness | TAU_AB1_42_ratio | -3.50 | 94 | -0.34 | 0.00 | All | Hemisphere | no |
| logAvgThickness | TAU_AB1_ratio | -4.00 | 94 | -0.38 | 0.00 | All | Hemisphere | no |
| logAvgThickness_age_decay | AB1_ratio | 1.30 | 94 | 0.13 | 0.19 | All | Hemisphere | yes |
| logAvgThickness_age_decay | AB1-40 | -1.60 | 94 | -0.16 | 0.12 | All | Hemisphere | yes |
| logAvgThickness_age_decay | AB1-42 | -0.07 | 94 | -0.01 | 0.95 | All | Hemisphere | yes |
| logAvgThickness_age_decay | Lipoxina | -0.41 | 92 | -0.04 | 0.69 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TAU | -2.80 | 94 | -0.28 | 0.01 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TAU_AB1_42_ratio | -1.60 | 94 | -0.16 | 0.11 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TAU_AB1_ratio | -2.30 | 94 | -0.24 | 0.02 | All | Hemisphere | yes |
| Diagnostic | morphological_parameter | clinical_test | t | df | Correlation | pvalue | ROI | Age_correction |
|---|---|---|---|---|---|---|---|---|
| AD | K | A7/A5 | 0.21 | 24 | 0.04 | 0.83 | Hemisphere | no |
| AD | K | COGNITIVE_INDEX | 0.70 | 24 | 0.14 | 0.49 | Hemisphere | no |
| AD | K | DIGIT SPAN BACK | -0.14 | 24 | -0.03 | 0.89 | Hemisphere | no |
| AD | K | TMT B-A | -1.40 | 24 | -0.28 | 0.17 | Hemisphere | no |
| AD | K_age_decay | A7/A5 | 0.58 | 24 | 0.12 | 0.57 | Hemisphere | yes |
| AD | K_age_decay | COGNITIVE_INDEX | 0.94 | 24 | 0.19 | 0.35 | Hemisphere | yes |
| AD | K_age_decay | DIGIT SPAN BACK | 0.19 | 24 | 0.04 | 0.85 | Hemisphere | yes |
| AD | K_age_decay | TMT B-A | -1.70 | 24 | -0.33 | 0.10 | Hemisphere | yes |
| AD | logAvgThickness | A7/A5 | 0.16 | 24 | 0.03 | 0.87 | Hemisphere | no |
| AD | logAvgThickness | COGNITIVE_INDEX | -0.51 | 24 | -0.10 | 0.61 | Hemisphere | no |
| AD | logAvgThickness | DIGIT SPAN BACK | 1.00 | 24 | 0.21 | 0.31 | Hemisphere | no |
| AD | logAvgThickness | TMT B-A | -0.09 | 24 | -0.02 | 0.93 | Hemisphere | no |
| AD | logAvgThickness_age_decay | A7/A5 | 0.86 | 24 | 0.17 | 0.40 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | COGNITIVE_INDEX | 0.16 | 24 | 0.03 | 0.87 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | DIGIT SPAN BACK | 1.70 | 24 | 0.32 | 0.11 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TMT B-A | -0.55 | 24 | -0.11 | 0.59 | Hemisphere | yes |
| MCI | K | A7/A5 | 1.40 | 64 | 0.17 | 0.17 | Hemisphere | no |
| MCI | K | COGNITIVE_INDEX | 0.88 | 62 | 0.11 | 0.38 | Hemisphere | no |
| MCI | K | DIGIT SPAN BACK | 0.55 | 64 | 0.07 | 0.58 | Hemisphere | no |
| MCI | K | TMT B-A | -0.01 | 64 | 0.00 | 0.99 | Hemisphere | no |
| MCI | K_age_decay | A7/A5 | 1.50 | 64 | 0.18 | 0.15 | Hemisphere | yes |
| MCI | K_age_decay | COGNITIVE_INDEX | 0.53 | 62 | 0.07 | 0.60 | Hemisphere | yes |
| MCI | K_age_decay | DIGIT SPAN BACK | 0.41 | 64 | 0.05 | 0.68 | Hemisphere | yes |
| MCI | K_age_decay | TMT B-A | 0.51 | 64 | 0.06 | 0.61 | Hemisphere | yes |
| MCI | logAvgThickness | A7/A5 | 1.70 | 64 | 0.21 | 0.10 | Hemisphere | no |
| MCI | logAvgThickness | COGNITIVE_INDEX | 0.29 | 62 | 0.04 | 0.77 | Hemisphere | no |
| MCI | logAvgThickness | DIGIT SPAN BACK | -0.67 | 64 | -0.08 | 0.51 | Hemisphere | no |
| MCI | logAvgThickness | TMT B-A | 1.00 | 64 | 0.13 | 0.31 | Hemisphere | no |
| MCI | logAvgThickness_age_decay | A7/A5 | 1.70 | 64 | 0.20 | 0.10 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | COGNITIVE_INDEX | -0.25 | 62 | -0.03 | 0.80 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | DIGIT SPAN BACK | -0.77 | 64 | -0.10 | 0.45 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TMT B-A | 1.90 | 64 | 0.23 | 0.07 | Hemisphere | yes |
| CTL | K | A7/A5 | 1.10 | 150 | 0.09 | 0.26 | Hemisphere | no |
| CTL | K | COGNITIVE_INDEX | 1.80 | 150 | 0.15 | 0.07 | Hemisphere | no |
| CTL | K | DIGIT SPAN BACK | 1.80 | 150 | 0.14 | 0.08 | Hemisphere | no |
| CTL | K | TMT B-A | -0.33 | 150 | -0.03 | 0.74 | Hemisphere | no |
| CTL | K_age_decay | A7/A5 | 0.01 | 150 | 0.00 | 0.99 | Hemisphere | yes |
| CTL | K_age_decay | COGNITIVE_INDEX | 1.10 | 150 | 0.09 | 0.26 | Hemisphere | yes |
| CTL | K_age_decay | DIGIT SPAN BACK | 1.30 | 150 | 0.10 | 0.21 | Hemisphere | yes |
| CTL | K_age_decay | TMT B-A | 0.97 | 150 | 0.08 | 0.33 | Hemisphere | yes |
| CTL | logAvgThickness | A7/A5 | 2.80 | 150 | 0.22 | 0.01 | Hemisphere | no |
| CTL | logAvgThickness | COGNITIVE_INDEX | 4.30 | 150 | 0.33 | 0.00 | Hemisphere | no |
| CTL | logAvgThickness | DIGIT SPAN BACK | 0.94 | 150 | 0.08 | 0.35 | Hemisphere | no |
| CTL | logAvgThickness | TMT B-A | -0.47 | 150 | -0.04 | 0.64 | Hemisphere | no |
| CTL | logAvgThickness_age_decay | A7/A5 | 1.20 | 150 | 0.10 | 0.24 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | COGNITIVE_INDEX | 3.30 | 150 | 0.26 | 0.00 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | DIGIT SPAN BACK | 0.23 | 150 | 0.02 | 0.82 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TMT B-A | 1.30 | 150 | 0.11 | 0.19 | Hemisphere | yes |
| AD | K | AB1_ratio | -1.20 | 10 | -0.35 | 0.26 | Hemisphere | no |
| AD | K | AB1-40 | 1.40 | 10 | 0.39 | 0.21 | Hemisphere | no |
| AD | K | AB1-42 | 1.10 | 10 | 0.34 | 0.28 | Hemisphere | no |
| AD | K | Lipoxina | 0.90 | 10 | 0.27 | 0.39 | Hemisphere | no |
| AD | K | TAU | 1.70 | 10 | 0.48 | 0.11 | Hemisphere | no |
| AD | K | TAU_AB1_42_ratio | 1.40 | 10 | 0.40 | 0.20 | Hemisphere | no |
| AD | K | TAU_AB1_ratio | 1.90 | 10 | 0.51 | 0.09 | Hemisphere | no |
| AD | K_age_decay | AB1_ratio | -1.20 | 10 | -0.35 | 0.27 | Hemisphere | yes |
| AD | K_age_decay | AB1-40 | 1.30 | 10 | 0.38 | 0.23 | Hemisphere | yes |
| AD | K_age_decay | AB1-42 | 1.10 | 10 | 0.33 | 0.30 | Hemisphere | yes |
| AD | K_age_decay | Lipoxina | 1.00 | 10 | 0.31 | 0.32 | Hemisphere | yes |
| AD | K_age_decay | TAU | 1.60 | 10 | 0.46 | 0.13 | Hemisphere | yes |
| AD | K_age_decay | TAU_AB1_42_ratio | 1.30 | 10 | 0.38 | 0.23 | Hemisphere | yes |
| AD | K_age_decay | TAU_AB1_ratio | 1.80 | 10 | 0.48 | 0.11 | Hemisphere | yes |
| AD | logAvgThickness | AB1_ratio | -0.46 | 10 | -0.14 | 0.66 | Hemisphere | no |
| AD | logAvgThickness | AB1-40 | 1.20 | 10 | 0.36 | 0.25 | Hemisphere | no |
| AD | logAvgThickness | AB1-42 | 1.40 | 10 | 0.40 | 0.20 | Hemisphere | no |
| AD | logAvgThickness | Lipoxina | -0.03 | 10 | -0.01 | 0.98 | Hemisphere | no |
| AD | logAvgThickness | TAU | 1.30 | 10 | 0.37 | 0.23 | Hemisphere | no |
| AD | logAvgThickness | TAU_AB1_42_ratio | 0.79 | 10 | 0.24 | 0.45 | Hemisphere | no |
| AD | logAvgThickness | TAU_AB1_ratio | 1.20 | 10 | 0.36 | 0.24 | Hemisphere | no |
| AD | logAvgThickness_age_decay | AB1_ratio | -0.06 | 10 | -0.02 | 0.95 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | AB1-40 | 0.78 | 10 | 0.24 | 0.45 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | AB1-42 | 0.91 | 10 | 0.28 | 0.38 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | Lipoxina | 0.23 | 10 | 0.07 | 0.82 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TAU | 0.96 | 10 | 0.29 | 0.36 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TAU_AB1_42_ratio | 0.71 | 10 | 0.22 | 0.50 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TAU_AB1_ratio | 0.90 | 10 | 0.27 | 0.39 | Hemisphere | yes |
| MCI | K | AB1_ratio | -0.27 | 24 | -0.05 | 0.79 | Hemisphere | no |
| MCI | K | AB1-40 | 2.00 | 24 | 0.37 | 0.06 | Hemisphere | no |
| MCI | K | AB1-42 | 2.90 | 24 | 0.51 | 0.01 | Hemisphere | no |
| MCI | K | Lipoxina | 0.50 | 24 | 0.10 | 0.63 | Hemisphere | no |
| MCI | K | TAU | -0.46 | 24 | -0.09 | 0.65 | Hemisphere | no |
| MCI | K | TAU_AB1_42_ratio | -1.80 | 24 | -0.35 | 0.08 | Hemisphere | no |
| MCI | K | TAU_AB1_ratio | -1.50 | 24 | -0.30 | 0.14 | Hemisphere | no |
| MCI | K_age_decay | AB1_ratio | -0.26 | 24 | -0.05 | 0.79 | Hemisphere | yes |
| MCI | K_age_decay | AB1-40 | 2.20 | 24 | 0.42 | 0.03 | Hemisphere | yes |
| MCI | K_age_decay | AB1-42 | 3.00 | 24 | 0.53 | 0.01 | Hemisphere | yes |
| MCI | K_age_decay | Lipoxina | 0.38 | 24 | 0.08 | 0.71 | Hemisphere | yes |
| MCI | K_age_decay | TAU | -0.05 | 24 | -0.01 | 0.96 | Hemisphere | yes |
| MCI | K_age_decay | TAU_AB1_42_ratio | -1.40 | 24 | -0.27 | 0.17 | Hemisphere | yes |
| MCI | K_age_decay | TAU_AB1_ratio | -1.10 | 24 | -0.22 | 0.29 | Hemisphere | yes |
| MCI | logAvgThickness | AB1_ratio | 0.03 | 24 | 0.01 | 0.98 | Hemisphere | no |
| MCI | logAvgThickness | AB1-40 | -0.53 | 24 | -0.11 | 0.60 | Hemisphere | no |
| MCI | logAvgThickness | AB1-42 | 0.97 | 24 | 0.19 | 0.34 | Hemisphere | no |
| MCI | logAvgThickness | Lipoxina | 0.77 | 24 | 0.16 | 0.45 | Hemisphere | no |
| MCI | logAvgThickness | TAU | -2.50 | 24 | -0.46 | 0.02 | Hemisphere | no |
| MCI | logAvgThickness | TAU_AB1_42_ratio | -1.60 | 24 | -0.31 | 0.12 | Hemisphere | no |
| MCI | logAvgThickness | TAU_AB1_ratio | -2.20 | 24 | -0.42 | 0.03 | Hemisphere | no |
| MCI | logAvgThickness_age_decay | AB1_ratio | -0.30 | 24 | -0.06 | 0.77 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | AB1-40 | -0.17 | 24 | -0.04 | 0.86 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | AB1-42 | 0.89 | 24 | 0.18 | 0.38 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | Lipoxina | 0.79 | 24 | 0.16 | 0.44 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TAU | -1.50 | 24 | -0.29 | 0.15 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TAU_AB1_42_ratio | -0.75 | 24 | -0.15 | 0.46 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TAU_AB1_ratio | -1.30 | 24 | -0.25 | 0.22 | Hemisphere | yes |
| CTL | K | AB1_ratio | 0.66 | 56 | 0.09 | 0.51 | Hemisphere | no |
| CTL | K | AB1-40 | -1.50 | 56 | -0.20 | 0.14 | Hemisphere | no |
| CTL | K | AB1-42 | -0.44 | 56 | -0.06 | 0.66 | Hemisphere | no |
| CTL | K | Lipoxina | -1.20 | 54 | -0.17 | 0.22 | Hemisphere | no |
| CTL | K | TAU | -2.00 | 56 | -0.26 | 0.05 | Hemisphere | no |
| CTL | K | TAU_AB1_42_ratio | -0.74 | 56 | -0.10 | 0.46 | Hemisphere | no |
| CTL | K | TAU_AB1_ratio | -1.00 | 56 | -0.13 | 0.32 | Hemisphere | no |
| CTL | K_age_decay | AB1_ratio | 0.77 | 56 | 0.10 | 0.44 | Hemisphere | yes |
| CTL | K_age_decay | AB1-40 | -1.40 | 56 | -0.18 | 0.18 | Hemisphere | yes |
| CTL | K_age_decay | AB1-42 | -0.46 | 56 | -0.06 | 0.65 | Hemisphere | yes |
| CTL | K_age_decay | Lipoxina | -0.76 | 54 | -0.10 | 0.45 | Hemisphere | yes |
| CTL | K_age_decay | TAU | -1.30 | 56 | -0.17 | 0.20 | Hemisphere | yes |
| CTL | K_age_decay | TAU_AB1_42_ratio | -0.05 | 56 | -0.01 | 0.96 | Hemisphere | yes |
| CTL | K_age_decay | TAU_AB1_ratio | -0.36 | 56 | -0.05 | 0.72 | Hemisphere | yes |
| CTL | logAvgThickness | AB1_ratio | 0.53 | 56 | 0.07 | 0.60 | Hemisphere | no |
| CTL | logAvgThickness | AB1-40 | -1.40 | 56 | -0.18 | 0.18 | Hemisphere | no |
| CTL | logAvgThickness | AB1-42 | -2.20 | 56 | -0.28 | 0.03 | Hemisphere | no |
| CTL | logAvgThickness | Lipoxina | -3.20 | 54 | -0.40 | 0.00 | Hemisphere | no |
| CTL | logAvgThickness | TAU | -2.50 | 56 | -0.31 | 0.02 | Hemisphere | no |
| CTL | logAvgThickness | TAU_AB1_42_ratio | -0.28 | 56 | -0.04 | 0.78 | Hemisphere | no |
| CTL | logAvgThickness | TAU_AB1_ratio | -0.94 | 56 | -0.12 | 0.35 | Hemisphere | no |
| CTL | logAvgThickness_age_decay | AB1_ratio | 0.51 | 56 | 0.07 | 0.61 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | AB1-40 | -1.10 | 56 | -0.14 | 0.28 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | AB1-42 | -2.50 | 56 | -0.31 | 0.02 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | Lipoxina | -2.20 | 54 | -0.29 | 0.03 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TAU | -1.40 | 56 | -0.18 | 0.18 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TAU_AB1_42_ratio | 0.82 | 56 | 0.11 | 0.42 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TAU_AB1_ratio | 0.04 | 56 | 0.00 | 0.97 | Hemisphere | yes |
| morphological_parameter | clinical_test | t | df | Correlation | ROI | Age_correction | pval.adj |
|---|---|---|---|---|---|---|---|
| K | A7/A5 | 5.800 | 240 | 0.350 | Hemisphere | no | 0.000 |
| K | COGNITIVE_INDEX | 6.700 | 240 | 0.400 | Hemisphere | no | 0.000 |
| K | DIGIT SPAN BACK | 4.100 | 240 | 0.250 | Hemisphere | no | 0.000 |
| K | TMT B-A | -4.800 | 240 | -0.290 | Hemisphere | no | 0.000 |
| K | A7/A5 | 4.300 | 240 | 0.260 | Hemisphere | yes | 0.000 |
| K | COGNITIVE_INDEX | 4.900 | 240 | 0.300 | Hemisphere | yes | 0.000 |
| K | DIGIT SPAN BACK | 3.100 | 240 | 0.190 | Hemisphere | yes | 0.010 |
| K | TMT B-A | -3.100 | 240 | -0.190 | Hemisphere | yes | 0.010 |
| logAvgThickness | A7/A5 | 6.700 | 240 | 0.390 | Hemisphere | no | 0.000 |
| logAvgThickness | COGNITIVE_INDEX | 6.800 | 240 | 0.400 | Hemisphere | no | 0.000 |
| logAvgThickness | DIGIT SPAN BACK | 3.200 | 240 | 0.200 | Hemisphere | no | 0.005 |
| logAvgThickness | TMT B-A | -3.500 | 240 | -0.220 | Hemisphere | no | 0.002 |
| logAvgThickness | A7/A5 | 4.200 | 240 | 0.260 | Hemisphere | yes | 0.000 |
| logAvgThickness | COGNITIVE_INDEX | 4.100 | 240 | 0.260 | Hemisphere | yes | 0.000 |
| logAvgThickness | DIGIT SPAN BACK | 1.800 | 240 | 0.110 | Hemisphere | yes | 0.307 |
| logAvgThickness | TMT B-A | -1.100 | 240 | -0.069 | Hemisphere | yes | 1.000 |
| K | AB1_ratio | 1.700 | 94 | 0.180 | Hemisphere | no | 0.167 |
| K | AB1-40 | -0.760 | 94 | -0.078 | Hemisphere | no | 0.894 |
| K | AB1-42 | 2.500 | 94 | 0.250 | Hemisphere | no | 0.031 |
| K | Lipoxina | 0.850 | 92 | 0.088 | Hemisphere | no | 0.795 |
| K | TAU | -2.600 | 94 | -0.260 | Hemisphere | no | 0.023 |
| K | TAU_AB1_42_ratio | -3.200 | 94 | -0.310 | Hemisphere | no | 0.004 |
| K | TAU_AB1_ratio | -2.800 | 94 | -0.280 | Hemisphere | no | 0.011 |
| K | AB1_ratio | 1.600 | 94 | 0.160 | Hemisphere | yes | 0.241 |
| K | AB1-40 | -0.280 | 94 | -0.029 | Hemisphere | yes | 1.000 |
| K | AB1-42 | 2.400 | 94 | 0.240 | Hemisphere | yes | 0.038 |
| K | Lipoxina | 1.000 | 92 | 0.110 | Hemisphere | yes | 0.619 |
| K | TAU | -1.700 | 94 | -0.170 | Hemisphere | yes | 0.189 |
| K | TAU_AB1_42_ratio | -2.300 | 94 | -0.230 | Hemisphere | yes | 0.044 |
| K | TAU_AB1_ratio | -2.000 | 94 | -0.200 | Hemisphere | yes | 0.094 |
| logAvgThickness | AB1_ratio | 2.000 | 94 | 0.200 | Hemisphere | no | 0.104 |
| logAvgThickness | AB1-40 | -2.100 | 94 | -0.210 | Hemisphere | no | 0.076 |
| logAvgThickness | AB1-42 | 0.840 | 94 | 0.086 | Hemisphere | no | 0.805 |
| logAvgThickness | Lipoxina | -0.510 | 92 | -0.053 | Hemisphere | no | 1.000 |
| logAvgThickness | TAU | -4.300 | 94 | -0.410 | Hemisphere | no | 0.000 |
| logAvgThickness | TAU_AB1_42_ratio | -3.500 | 94 | -0.340 | Hemisphere | no | 0.001 |
| logAvgThickness | TAU_AB1_ratio | -4.000 | 94 | -0.380 | Hemisphere | no | 0.000 |
| logAvgThickness | AB1_ratio | 1.300 | 94 | 0.130 | Hemisphere | yes | 0.384 |
| logAvgThickness | AB1-40 | -1.600 | 94 | -0.160 | Hemisphere | yes | 0.231 |
| logAvgThickness | AB1-42 | -0.069 | 94 | -0.007 | Hemisphere | yes | 1.000 |
| logAvgThickness | Lipoxina | -0.410 | 92 | -0.042 | Hemisphere | yes | 1.000 |
| logAvgThickness | TAU | -2.800 | 94 | -0.280 | Hemisphere | yes | 0.011 |
| logAvgThickness | TAU_AB1_42_ratio | -1.600 | 94 | -0.160 | Hemisphere | yes | 0.227 |
| logAvgThickness | TAU_AB1_ratio | -2.300 | 94 | -0.240 | Hemisphere | yes | 0.042 |
| K | DIGIT SPAN BACK | 2.400 | 240 | 0.160 | Frontal lobe | yes | 0.061 |
| K | relogio | -1.100 | 240 | -0.070 | Frontal lobe | yes | 1.000 |
| K | TMT B-A | -2.000 | 240 | -0.130 | Frontal lobe | yes | 0.172 |
| K | DIGIT SPAN BACK | 3.400 | 240 | 0.220 | Frontal lobe | no | 0.003 |
| K | relogio | 0.240 | 240 | 0.016 | Frontal lobe | no | 1.000 |
| K | TMT B-A | -3.100 | 240 | -0.200 | Frontal lobe | no | 0.009 |
| logAvgThickness | DIGIT SPAN BACK | 2.500 | 240 | 0.160 | Frontal lobe | no | 0.057 |
| logAvgThickness | relogio | 2.200 | 240 | 0.140 | Frontal lobe | no | 0.162 |
| logAvgThickness | TMT B-A | -3.300 | 240 | -0.210 | Frontal lobe | no | 0.005 |
| logAvgThickness | DIGIT SPAN BACK | 1.100 | 240 | 0.071 | Frontal lobe | yes | 1.000 |
| logAvgThickness | relogio | 0.950 | 240 | 0.062 | Frontal lobe | yes | 1.000 |
| logAvgThickness | TMT B-A | -1.100 | 240 | -0.071 | Frontal lobe | yes | 1.000 |
| K | relogio | -0.390 | 240 | -0.026 | Parietal lobe | yes | 1.000 |
| K | relogio | -0.260 | 240 | -0.017 | Parietal lobe | no | 1.000 |
| logAvgThickness | relogio | 1.400 | 240 | 0.089 | Parietal lobe | no | 1.000 |
| logAvgThickness | relogio | -0.310 | 240 | -0.020 | Parietal lobe | yes | 1.000 |
| K | relogio | -1.800 | 240 | -0.120 | Occipital lobe | yes | 0.410 |
| K | relogio | -0.670 | 240 | -0.044 | Occipital lobe | no | 1.000 |
| logAvgThickness | relogio | -1.200 | 240 | -0.080 | Occipital lobe | no | 1.000 |
| logAvgThickness | relogio | -2.200 | 240 | -0.140 | Occipital lobe | yes | 0.192 |
| K | A7/A5 | 3.100 | 240 | 0.200 | Temporal lobe | yes | 0.008 |
| K | A7/A5 | 4.900 | 240 | 0.300 | Temporal lobe | no | 0.000 |
| logAvgThickness | A7/A5 | 7.500 | 240 | 0.430 | Temporal lobe | no | 0.000 |
| logAvgThickness | A7/A5 | 5.700 | 240 | 0.340 | Temporal lobe | yes | 0.000 |
## Method: maximize_boot_metric
## Predictor: K
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 1000
##
## AUC n n_pos n_neg
## 0.8442 180 26 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5402 1.4341 0.8167 0.5769 0.8571 15 11 22 132
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5705655 -0.5554250 -0.5369054 -0.5277599 -0.5279802 -0.5160266
## AD -0.5705655 -0.5675134 -0.5595947 -0.5523513 -0.5467767 -0.5315364
## MCI NA NA NA NA NaN NA
## CTL -0.5553652 -0.5484493 -0.5346425 -0.5249779 -0.5248068 -0.5145646
## 95% Max. SD NAs
## -0.5032479 -0.4974075 0.01602744 0
## -0.5260303 -0.5225081 0.01543894 0
## NA NA NA 0
## -0.5028530 -0.4974075 0.01383501 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.55 -0.55 -0.54 -0.54 -0.54 -0.53 -0.53 -0.52 0.01
## AUC_b 0.67 0.78 0.82 0.85 0.84 0.87 0.90 0.96 0.04
## AUC_oob 0.60 0.74 0.81 0.85 0.84 0.88 0.93 1.00 0.06
## sum_sens_spec_b 1.12 1.26 1.37 1.46 1.46 1.55 1.67 1.85 0.12
## sum_sens_spec_oob 0.85 1.19 1.33 1.42 1.41 1.50 1.62 1.81 0.13
## acc_b 0.48 0.65 0.72 0.78 0.78 0.85 0.92 0.96 0.08
## acc_oob 0.49 0.62 0.71 0.78 0.77 0.84 0.90 0.97 0.09
## sensitivity_b 0.40 0.50 0.58 0.63 0.66 0.71 0.88 1.00 0.11
## sensitivity_oob 0.00 0.33 0.50 0.60 0.62 0.75 0.93 1.00 0.19
## specificity_b 0.43 0.63 0.73 0.81 0.80 0.89 0.95 0.99 0.10
## specificity_oob 0.36 0.58 0.71 0.81 0.79 0.89 0.96 1.00 0.12
## cohens_kappa_b 0.08 0.16 0.25 0.33 0.36 0.47 0.65 0.83 0.15
## cohens_kappa_oob -0.08 0.13 0.23 0.31 0.32 0.39 0.53 0.78 0.12
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## Method: maximize_boot_metric
## Predictor: K
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 999
##
## AUC n n_pos n_neg
## 0.9909 169 15 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5493 1.9009 0.9645 0.9333 0.9675 14 1 5 149
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5705655 -0.5560824 -0.5382005 -0.5269957 -0.5278329 -0.5150280
## AD -0.5705655 -0.5687677 -0.5638509 -0.5574780 -0.5589016 -0.5537495
## MCI NA NA NA NA NaN NA
## CTL -0.5553652 -0.5484493 -0.5346425 -0.5249779 -0.5248068 -0.5145646
## 95% Max. SD NAs
## -0.5031343 -0.4974075 0.016508797 0
## -0.5508475 -0.5480362 0.006612885 0
## NA NA NA 0
## -0.5028530 -0.4974075 0.013835011 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.56 -0.55 -0.55 -0.55 -0.55 -0.55 -0.55 -0.55 0.00
## AUC_b 0.97 0.98 0.99 0.99 0.99 1.00 1.00 1.00 0.01
## AUC_oob 0.93 0.97 0.99 0.99 0.99 1.00 1.00 1.00 0.01
## sum_sens_spec_b 1.48 1.78 1.87 1.91 1.91 1.97 1.99 2.00 0.07
## sum_sens_spec_oob 1.14 1.62 1.82 1.93 1.88 1.97 1.98 2.00 0.13
## acc_b 0.89 0.93 0.95 0.97 0.97 0.98 0.99 1.00 0.02
## acc_oob 0.86 0.92 0.95 0.97 0.96 0.97 0.99 1.00 0.02
## sensitivity_b 0.50 0.83 0.91 0.94 0.94 1.00 1.00 1.00 0.06
## sensitivity_oob 0.14 0.67 0.83 1.00 0.92 1.00 1.00 1.00 0.14
## specificity_b 0.89 0.93 0.95 0.97 0.97 0.98 0.99 1.00 0.02
## specificity_oob 0.86 0.91 0.95 0.97 0.96 0.98 1.00 1.00 0.03
## cohens_kappa_b 0.35 0.62 0.74 0.82 0.81 0.89 0.97 1.00 0.11
## cohens_kappa_oob 0.21 0.54 0.70 0.78 0.77 0.86 0.94 1.00 0.13
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## Method: maximize_boot_metric
## Predictor: logAvgThickness
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 999
##
## AUC n n_pos n_neg
## 0.9013 169 15 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3807 1.6636 0.858 0.8 0.8636 12 3 21 133
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3493389 0.3663483 0.3840704 0.3973886 0.3966942 0.4084630 0.4271974
## AD 0.3493389 0.3540406 0.3602967 0.3727388 0.3712982 0.3778763 0.3949573
## MCI NA NA NA NA NaN NA NA
## CTL 0.3566807 0.3720598 0.3863418 0.3992158 0.3991678 0.4094520 0.4280006
## Max. SD NAs
## 0.4411069 0.01853311 0
## 0.3957719 0.01371868 0
## NA NA 0
## 0.4411069 0.01704522 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.37 0.38 0.38 0.38 0.38 0.38 0.39 0.39 0.00 0
## AUC_b 0.76 0.84 0.88 0.91 0.90 0.93 0.96 1.00 0.04 0
## AUC_oob 0.53 0.80 0.87 0.91 0.90 0.94 0.98 1.00 0.06 0
## sum_sens_spec_b 1.12 1.42 1.58 1.67 1.66 1.75 1.86 2.00 0.13 0
## sum_sens_spec_oob 0.89 1.24 1.51 1.63 1.61 1.72 1.88 1.96 0.18 0
## acc_b 0.60 0.72 0.81 0.86 0.85 0.89 0.93 1.00 0.07 0
## acc_oob 0.55 0.70 0.80 0.85 0.83 0.88 0.92 0.97 0.07 0
## sensitivity_b 0.50 0.67 0.75 0.81 0.81 0.87 0.94 1.00 0.08 0
## sensitivity_oob 0.00 0.33 0.67 0.80 0.76 1.00 1.00 1.00 0.21 0
## specificity_b 0.59 0.71 0.81 0.87 0.85 0.90 0.94 1.00 0.07 0
## specificity_oob 0.51 0.68 0.80 0.86 0.84 0.90 0.95 1.00 0.08 0
## cohens_kappa_b 0.03 0.18 0.33 0.44 0.43 0.53 0.67 1.00 0.15 0
## cohens_kappa_oob -0.09 0.16 0.28 0.37 0.37 0.46 0.58 0.77 0.13 0
## Method: maximize_boot_metric
## Predictor: K
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 984
##
## AUC n n_pos n_neg
## 0.644 165 11 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5259 1.3312 0.5333 0.8182 0.513 9 2 75 79
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5553652 -0.5483897 -0.5338496 -0.5261242 -0.5251692 -0.5148557
## AD -0.5341828 -0.5340162 -0.5328250 -0.5310336 -0.5302428 -0.5289468
## MCI NA NA NA NA NaN NA
## CTL -0.5553652 -0.5484493 -0.5346425 -0.5249779 -0.5248068 -0.5145646
## 95% Max. SD NAs
## -0.5030930 -0.4974075 0.013462523 0
## -0.5239095 -0.5225081 0.003668016 0
## NA NA NA 0
## -0.5028530 -0.4974075 0.013835011 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.53 -0.53 -0.53 -0.53 -0.53 -0.52 -0.52 -0.52 0.00
## AUC_b 0.48 0.57 0.62 0.65 0.64 0.67 0.72 0.78 0.05
## AUC_oob 0.37 0.54 0.60 0.64 0.64 0.69 0.74 0.84 0.06
## sum_sens_spec_b 0.96 1.20 1.31 1.39 1.39 1.47 1.57 1.73 0.12
## sum_sens_spec_oob 0.41 1.00 1.23 1.37 1.33 1.47 1.55 1.64 0.18
## acc_b 0.38 0.45 0.50 0.55 0.55 0.60 0.67 0.75 0.07
## acc_oob 0.34 0.44 0.50 0.54 0.54 0.59 0.66 0.73 0.07
## sensitivity_b 0.50 0.71 0.81 0.87 0.86 0.91 1.00 1.00 0.08
## sensitivity_oob 0.00 0.40 0.67 0.83 0.80 1.00 1.00 1.00 0.22
## specificity_b 0.36 0.42 0.48 0.53 0.53 0.58 0.65 0.73 0.07
## specificity_oob 0.31 0.41 0.47 0.52 0.52 0.58 0.67 0.80 0.08
## cohens_kappa_b -0.01 0.04 0.07 0.09 0.10 0.13 0.19 0.27 0.05
## cohens_kappa_oob -0.18 0.00 0.05 0.08 0.08 0.11 0.17 0.24 0.05
## NAs
## 0
## 0
## 6
## 0
## 6
## 0
## 0
## 0
## 6
## 0
## 0
## 0
## 0
## Method: maximize_boot_metric
## Predictor: logAvgThickness
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 970
##
## AUC n n_pos n_neg
## 0.778 165 11 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3915 1.3831 0.6606 0.7273 0.6558 8 3 53 101
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3566807 0.3693055 0.3855580 0.3981156 0.3980329 0.4087728 0.4273469
## AD 0.3593543 0.3606476 0.3747296 0.3851112 0.3821438 0.3906316 0.3981935
## MCI NA NA NA NA NaN NA NA
## CTL 0.3566807 0.3720598 0.3863418 0.3992158 0.3991678 0.4094520 0.4280006
## Max. SD NAs
## 0.4411069 0.01732566 0
## 0.4034812 0.01342109 0
## NA NA 0
## 0.4411069 0.01704522 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.38 0.38 0.39 0.39 0.39 0.39 0.40 0.40 0.00 0
## AUC_b 0.58 0.67 0.74 0.78 0.77 0.82 0.87 0.96 0.06 0
## AUC_oob 0.40 0.63 0.72 0.78 0.78 0.84 0.92 1.00 0.09 5
## sum_sens_spec_b 0.99 1.26 1.38 1.46 1.45 1.53 1.64 1.81 0.12 0
## sum_sens_spec_oob 0.61 0.94 1.21 1.40 1.38 1.58 1.69 1.81 0.24 5
## acc_b 0.42 0.59 0.64 0.68 0.68 0.72 0.79 0.91 0.07 0
## acc_oob 0.40 0.57 0.64 0.67 0.68 0.72 0.78 0.87 0.07 0
## sensitivity_b 0.29 0.58 0.71 0.79 0.78 0.86 0.93 1.00 0.11 0
## sensitivity_oob 0.00 0.20 0.50 0.75 0.70 1.00 1.00 1.00 0.28 5
## specificity_b 0.40 0.57 0.63 0.67 0.68 0.72 0.80 0.92 0.07 0
## specificity_oob 0.36 0.55 0.62 0.67 0.68 0.73 0.81 0.95 0.08 0
## cohens_kappa_b 0.00 0.06 0.11 0.15 0.16 0.19 0.26 0.42 0.06 0
## cohens_kappa_oob -0.14 -0.02 0.06 0.12 0.12 0.17 0.26 0.40 0.08 0